Abstract
Objective
To estimate the global prevalence of low resilience among the general population and health professionals during the COVID-19 pandemic.
Methods
Embase, Ovid-MEDLINE, PubMed, Scopus, Web of Science, CINAHL, WHO COVID-19 databases, and grey literature were searched for studies from January 1, 2020, to August 22, 2022. Hoy's assessment tool was used to assess for risk of bias. Meta-analysis and moderator analysis was performed using the Generalized Linear Mixed Model with a corresponding 95 % confidence interval (95 % CI) adopting the random-effect model in R software. Between-study heterogeneity was measured using I2 and τ2 statistics.
Results
Overall, 44 studies involving 51,119 participants were identified. The pooled prevalence of low resilience was 27.0 % (95 % CI: 21.0 %–33.0 %) with prevalence among the general population being 35.0 % (95 % CI: 28.0 %–42.0 %) followed by 23.0 % (95 % CI: 16.0 %–30.9 %) for health professionals. The 3-month trend analysis of the prevalence of low resilience beginning January 2020 to June 2021 revealed upward then downward patterns among overall populations. The prevalence of low resilience was higher in females, studied during the delta variant dominant period, frontline health professionals, and undergraduate degree education.
Limitations
Study outcomes showed high heterogeneity; however, sub-group and meta-regression analyses were conducted to identify potential moderating factors.
Conclusions
Globally, 1 out of 4 people among the general population and health professionals experienced low resilience due to COVID-19 adversity. The prevalence of low resilience was twice as much among the general population compared to health professionals. These findings provide information for policymakers and clinicians in the development and implementation of resilience-enhancing programs.
Keywords: Low resilience, COVID-19, Health professionals, General population
1. Introduction
The Coronavirus disease 2019 (COVID-19) outbreak has been the most significant global health crisis since the 1918 influenza pandemic. Aside from endangering human life, the COVID-19 pandemic had a tremendous impact on society, the economy, and mental health, creating uncertainty. Following the uncertainty generated by COVID-19, there was a feeling that future developments are inherently unpredictable, which has implications for the psychological vulnerability of the global population. Recent meta-analyses revealed a significant prevalence of stress, anxiety, and depression throughout the COVID-19 pandemic among health professionals and the general population (Ghahramani et al., 2022; Salari et al., 2020; Wu et al., 2021). Furthermore, the prevalence of post-traumatic stress disorder (PTSD) among COVID-19 patients, health professionals, and the general population has been estimated to be 17.52 % (Yunitri et al., 2022). Thus, resilience may serve as a protective factor against mental health issues given that not everyone exposed to COVID-19 develops mental health issues (Horn and Feder, 2018).
Resilience is a phenomena or process that demonstrates generally positive adaption despite considerable adversity or trauma (Luthar et al., 2014). The difficulties brought on by COVID-19, such as health issues, social isolation, loneliness, the loss of loved ones, and uncertainties due to new emerging COVID-19 variants have become significant COVID-19-associated adversities globally. The holistic vulnerability-resilience model comprises four essential elements including (1) exposure (relative to pre-shock attributes), (2) sensitivity (relative to negative impacts caused by shock), (3) capacity of response (relative to pre-shock attributes), and (4) adaptive capacity (relative to positive responses to shock), which could be used as the basis to comprehend global change caused by the COVID-19 pandemic (Faulkner et al., 2020).
The COVID-19 pandemic has been demonstrated to be associated with a high degree of mental distress that has shown a negative association with resilience in the general population and among health professionals (Xiong et al., 2020). As such, resilience might be similar or vary within and between populations, with the processes that predict positive development being sensitive to personal, contextual, and cultural diversity (Ungar, 2013). Therefore, in order to see the different contextual mechanisms of resilience and to determine the population's adaptive capacity in response to the COVID-19 pandemic-associated adversities, it is crucial and necessary to explore and examine the prevalence of low resilience among the general population and health professionals a result of significant differences in exposure to the COVID-19 between these two groups.
Understanding resilience is useful in generating evidence for developing resilience-strengthening programs, monitoring, and increasing awareness of society, especially during the pandemic. A prior meta-analysis revealed that the prevalence of low resilience among healthcare professionals was estimated at 26 % pre-pandemic and in the early phases of the COVID-19 pandemic (Cheng et al., 2022). However, the previous meta-analysis included limited number of studies and thus, more studies have been conducted in both the general population and among health professionals, which could provide more comprehensive evidence on the prevalence of low resilience among the general population and among health professionals during the COVID-19 pandemic. Low resilience increases the risk of vulnerability, mental distress, and development of psychological sequelae, and in the context of the COVID-19 pandemic, a comprehensive meta-analysis study to explore and examine the prevalence of low resilience among the general population and health professionals is necessary and timely. Therefore, to extend previous knowledge and address the current research gap, we performed a comprehensive meta-analysis to examine and estimate the prevalence of low resilience during COVID-19 among the general population and health professionals.
2. Methods
This meta-analysis was reported according to the updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 and the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. For scientific integrity, the study protocol of this meta-analysis was registered with PROSPERO, International prospective register of systematic reviews (CRD42022330048).
2.1. Eligibility criteria
Quantitative observational studies meeting the following inclusion criteria were included: (1) investigated resilience in health professionals or general population, (2) study conducted during the COVID-19 pandemic period, (3) utilized valid assessment tools for measuring resilience, and (4) presented aggregate prevalence or provided adequate raw data for calculation. Studies were excluded if they: (1) used non-validated instruments and (2) the full text was inaccessible and the author could not be reached. The health professional population consisted of persons who were trained and qualified to give healthcare services to patients including nurses, physicians, psychotherapists, psychologist, nutritionist, pharmacists, midwives, radiologists, nursing aids, health care technicians, and allied health professionals whereas the general population consisted of non-health professionals including university students, family of health workers, patients, and their relatives.
2.2. Search strategy
Embase, Ovid-MEDLINE, PubMed, Scopus, Web of Science, CINAHL, and WHO COVID-19 databases were searched from January 1, 2020, to August 22, 2022. For a comprehensive and thorough search, the following keywords and medical subject headings (MeSH) terms were utilized: “resilience”, “psychological resilience”, “resiliency”, “pandemics”, “COVID-19”, and “coronavirus disease 2019” with a comprehensive search string described in Supplement 1. Other potentially eligible studies were identified by examining the reference lists of previously relevant published studies followed by a search in Google Scholar and grey literature (Fig. 1).
Fig. 1.
PRISMA flow diagram.
2.3. Study selection
Studies from the databases and manual searches were thoroughly screened using EndNote version 20 based on the inclusion and exclusion criteria. Both manually and electronically, duplicates were removed. Then, two independent reviewers examined the titles and abstracts of each study to identify full texts of potentially included studies. Study authors were contacted through e-mail if their full paper was not accessible. Throughout the course of the selection procedure, any differences between the two reviewers in the screening process were handled through discussion with a third expert reviewer.
2.4. Data extraction
Two independent reviewers extracted the data to confirm its validity and quality. The following data categories were extracted; author, country, year of publication, study design, population, age, sample size, gender, education, marital status, role as COVID-19 frontline health professionals, study period, instrument cut-off point for low resilience, and prevalence of low resilience. When there was missing data in the published studies, authors were contacted for more information to ensure that most of the eligible studies were included.
2.5. Risk of bias assessment
To assess the included studies' quality, two raters independently assessed each study using the risk of bias evaluation tool developed by Hoy et al. (2012). This is a 10-item evaluation instrument, with each item graded 1 for low risk and 0 for high risk. The total score ranges from 0 to 10 categorized into low (9–10), moderate (7–8), and high (0–6) risk of bias (Lundorff et al., 2017) (Table 1 ). A third expert reviewer was consulted for discussion if there was a disparity in the data between the two raters, and the Cohen's Kappa test was utilized for the level of agreement (McHugh, 2012).
Table 1.
Characteristics of the included studies.
| No | Authors (year) | Country | Study design | Population, education level, role as frontline health professional | Sample | Study period/variant dominant | Instrument/cut-off low resilience | Low resilience (%) | Study quality |
|---|---|---|---|---|---|---|---|---|---|
| 1. | Alameddine et al. (2021a) | Lebanon | Cross-sectional | Health professional
|
N: 265 Mean age (SD): NI Gender:
|
March 2020/Alpha | CD-RISC 25/1st quartile | 70 (26.4 %) | 8 – M |
| 2. | Alameddine et al. (2021b) | Lebanon | Cross-sectional | Health professional
|
N: 511 Mean age (SD): NI Gender:
|
July–October 2020/Alpha | CD-RISC 25/1st quartile | 107 (20.9 %) | 8 – M |
| 3. | Aldhahi et al. (2021) | Saudi Arabia | Cross-sectional | General population | N: 385 Mean age (SD): NI Gender:
|
May–August 2020/Alpha | CD-RISC 10/<24 | 163 (42.3 %) | 8 – M |
| 4. | Antonijevic et al. (2020) | Serbia | Cross-sectional | Health professional
|
N: 1450 Mean age (SD): 40.4 (NI) Gender:
|
March 2020/Alpha | BRCS/<13 | 521 (35.9 %) | 8 – M |
| 5. | Baptista et al. (2021) | Portugal | Cross-sectional | Health professional
|
N: 214 Mean age (SD): 38.6 (11.3) Gender:
|
May–June 2020/Alpha | RS-25/<121 | 49 (22.9 %) | 8 – M |
| 6. | Bates et al. (2021) | United Kingdom | Cross-sectional | Health professional
|
N: 117 Mean age (SD): NI Gender:
|
April 2020/Alpha | BRS/<3 | 21 (17.9 %) | 6 – H |
| 7. | Borges et al. (2022) | Mexico | Cross-sectional | Health professional
|
N: 2127 Mean age (SD): 37.6 (9.5) Gender:
|
May–July 2020/Alpha | BRS/lowest tertile | 766 (36.0 %) | 9 – L |
| 8. | de-Torres García et al. (2021) | Spain | Cross-sectional | General population
|
N: 17 Mean age: 44.5 (12.4) Gender:
|
NI | CD-RISC 10/<27 | 9 (52.9 %) | 6 – H |
| 9. | Duarte et al. (2020) | Portugal | Cross-sectional | Health professional
|
N: 2008 Mean age (SD): 38 (10) Gender:
|
March–June 2020/Alpha | RS-25/<121 | 428 (21.3 %) | 8 – M |
| 10. | Forycka et al. (2022) | Poland | Cross-sectional | General population
|
N: 1032 Mean age: NI Gender:
|
January–February 2021/Delta | RS-14/<65 | 465 (45.1 %) | 8 – M |
| 11. | Franck et al. (2022) | Belgium | Cross-sectional | Health professional
|
N: 1376 Mean age (SD): 40.1 (11.4) Gender:
|
April 2020/Alpha | CD-RISC 10/1st quartile | 660 (47.9 %) | 9 – L |
| 12. | Huang et al. (2020) | China | Cross-sectional | Health professional
|
N: 364 Mean age (SD): 44.3 (8.9) Gender:
|
February 2020/Alpha | CD-RISC 25/<50 | 59 (16.2 %) | 8 – M |
| 13. | Ionescu et al. (2021) | Romania | Cross-sectional | General population | N: 440 Mean age (SD): NI Gender:
|
February–April 2021/Delta | CD-RISC 10/1st quartile | 145 (32.9 %) | 8 – M |
| 14. | Jácome et al. (2021) | Portugal | Cross-sectional | Health professional
|
N: 511 Mean age (range): 33.7 (2.2) Gender:
|
May–June 2020/Alpha | RS-25/<121 | 94 (18.4 %) | 9 – L |
| 15. | Jose et al. (2020) | India | Cross-sectional | Health professional
|
N: 120 Mean age (SD): 29 (4.4) Gender:
|
August 2020/Alpha | CD-RISC 25/<65 | 19 (15.8 %) | 8 – M |
| 16. | Kelker et al. (2021) | United States of America | Cross-sectional | Health professional
|
N: 113 Mean age (SD): 40.8 (9.4) Gender:
|
April 2020/Alpha | BRS/<3 | 11 (9.7 %) | 8 – M |
| 17. | Khalaf et al. (2020) | Egypt | Cross-sectional | Health professional
|
N: 170 Mean age (SD): 36.5 (5.1) Gender:
|
March–May 2020/Alpha | BRCS/<13 | 85 (50.0 %) | 7 – M |
| 18. | Konlan et al. (2022) | Ghana | Cross-sectional | Health professional
|
N: 1264 Mean age (SD): 40.8 (8.3) Gender:
|
March–November 2020/Alpha | BRS/<3 | 326 (25.8 %) | 9 – L |
| 19. | Lara-Cabrera et al. (2021) | Spain | Cross-sectional | Health professional
|
N: 214 Mean age (SD): 40.3 (11.6) Gender:
|
June 2020/Alpha | RS-14/<65 | 34 (15.9 %) | 8 – M |
| 20. | LoGiudice and Bartos (2021) | United States of America | Cross-sectional | Health professional
|
N: 43 Mean age (SD): 40.9 (NI) Gender:
|
May 2020–June 2020/Alpha | BRCS/<13 | 11 (25.6 %) | 7 – M |
| 21. | Lucena et al. (2022) | Brazil | Cross-sectional | General populations | N: 300 Mean age (SD): 63.5 (13.1) Gender:
|
November 2020–February 2021/Delta | BRCS/<13 | 144 (48 %) | 8 – M |
| 22. | Manzanares et al. (2021) | Spain | Cross-sectional | Health professional
|
N: 686 Mean age (SD): 39.4 (11.8) Gender:
|
May 2020/Alpha | RS-14/<49 | 11 (1.6 %) | 8 – M |
| 23. | Meda-Lara et al. (2022) | Mexico | Cross-sectional | General population | N: 666 Mean age: 39.9 (13.4) Gender:
|
April–May 2020/Alpha | CD-RISC 10/<27 | 210 (31.5 %) | 7 – M |
| 24. | Pappa et al. (2021) | United Kingdom | Cross-sectional | Health professional
|
N: 387 Mean age (SD): 33.4 (13.7) Gender:
|
June–July 2020/Alpha | RS-14/<65 | 20 (5.17 %) | 7 – M |
| 25. | Penacoba et al. (2021) | Spain | Cross-sectional | Health professional
|
N: 448 Mean age (SD): 39.7 (10.5) Gender:
|
March–May 2020/Alpha | RS-14/<49 | 26 (5.8 %) | 7 – M |
| 26. | da Silva Pigati et al. (2022) | Brazil | Cross-sectional | Health professional
|
N: 519 Mean age (SD): - Gender:
|
August–October 2020/Alpha | RS-14/<65 | 145 (27.9 %) | 6 – H |
| 27. | Pinho et al. (2022) | Brazil | Cross-sectional | Health professional
|
N: 1313 Mean age (SD): 27.8 (4.4) Gender:
|
July–September 2020/Alpha | BRCS/<13 | 813 (61.9 %) | 8 – M |
| 28. | Quintiliani et al. (2022) | Italy | Cross-sectional | General population
|
N: 955 Mean age: NI Gender:
|
March–May 2020/Alpha | RS-14/<65 | 225 (23.6 %) | 6 – H |
| 29. | Riehm et al. (2021) | United States of America | Cross-sectional | General population | N: 6008 Mean age (SD): NI Gender:
|
March 2020/Alpha | BRS/<3 | 1037 (17.3 %) | 8 – M |
| 30. | Roberts et al. (2021) | United Kingdom | Cross-sectional | Health professional
|
N: 180 Mean age (SD): 45.1 (9.8) Gender:
|
May–June 2020/Alpha | RS-14/<65 | 11 (6.1 %) | 8 – M |
| 31. | Román-Mata et al. (2020) | Spain | Cross-sectional | General population | N: 1176 Mean age (SD): 35.3 (11.9) Gender:
|
March 2020/Alpha | CD-RISC 10/1st quartile | 312 (26.5 %) | 9 – L |
| 32. | Sabrina et al. (2021) | Bangladesh | Cross-sectional | General population
|
N: 327 Mean age (SD): 22.5 (1.7) Gender:
|
October–November 2021/Delta | BRCS/<13 | 197 (60.2 %) | 7 – M |
| 33. | Sachdeva et al. (2022) | India | Cross-sectional | General population
|
N: 150 Mean age: NI Gender:
|
NI | BRCS/<13 | 80 (53.3 %) | 8 – M |
| 34. | Sakr et al. (2022) | Lebanon | Cross-sectional | Health professional
|
N: 66 Mean age: 34.4 (8.3) Gender:
|
March–June 2021/Delta | RS-25/<115 | 8 (8.7 %) | 6 – H |
| 35. | Sampogna et al. (2021) | Italy | Cross-sectional | General population | N: 20,720 Mean age (SD): 40.4 (14.3) Gender:
|
March–May 2020/Alpha | CD-RISC 10/<29 | 10,775 (52.0 %) | 9 – L |
| 36. | Sehsah et al. (2021) | Egypt | Cross-sectional | Health professional
|
N: 714 Mean age (SD): NI Gender:
|
June 2020/Alpha | BRCS/<13 | 332 (46.5 %) | 9 – L |
| 37. | Serrano et al. (2021) | Spain | Cross-sectional | General population
|
N: 253 Mean age: NI Gender:
|
April–May 2020/Alpha | CD-RISC 23/<7.45 | 76 (30.0 %) | 6 – H |
| 38. | Sińska et al. (2021) | Poland | Cross-sectional | General population | N: 1082 Mean age (SD): 31.6 (11.9) Gender:
|
March–April 2020/Alpha | BRCS/<13 | 270 (25.0 %) | 9 – L |
| 39. | Stocchetti et al. (2021) | Italy | Cross-sectional | Health professional
|
N: 136 Mean age (SD): 39.1 (NI) Gender:
|
January 2021/Delta | RS-14/<65 | 24 (17.6 %) | 6 – H |
| 40. | Tsehay et al. (2020) | Ethiopia | Cross-sectional | Health professional
|
N: 423 Mean age (SD): 34.5 (8.4) Gender:
|
June–July 2020/Alpha | BRCS/<13 | 301 (71.2 %) | 9 – L |
| 41. | Verdolini et al. (2021) | Spain | Cross-sectional | General population
|
N: 530 Mean age (SD): 44.7 (13.7) Gender:
|
May–June 2020/Alpha | BRS/<3 | 162 (30.5 %) | 7 – M |
| 42. | Weitzel et al. (2021) | Germany | Cross-sectional | General population | N: 954 Mean age (SD): 75.5 (7.1) Gender:
|
April 2020/Alpha | BRS/<3 | 141 (14.7 %) | 8 – M |
| 43. | Zakeri et al. (2021) | Iran | Cross-sectional | Health professional
|
N: 185 Mean age (SD): NI Gender:
|
March–April 2020/Alpha | CD-RISC 25/<50 | 33 (17.8 %) | 6 – H |
| 44. | Zhang et al. (2022) | China | Cross-sectional | Health professional
|
N: 200 Mean age (SD): 32.0 (5) Gender:
|
February–March 2021/Delta | CD-RISC 25/<81 | 152 (76.0 %) | 7 – M |
Abbreviations: N: Study size; SD: standard deviation; CD-RISC: Connor Davidson Resilience Scale; BRCS: Brief Resilience Coping Scale, RS: The Resilience Scale, BRS: Brief Resilience Scale; L: low risk of bias, M: moderate risk of bias, H: high risk of bias.
2.6. Data analysis
A meta-analysis of proportions was performed using the metaprop function in the R-software package version 4.4.2 (R-CoreTeam, 2022). Data analysis was conducted using the generalized linear mixed model (GLMM) (Lin and Chu, 2020). Prevalence rates were calculated by dividing the number of individuals fulfilling the threshold for low resilience by the total number of individuals sampled.
The primary outcome was reported in proportion format with its corresponding 95 % confidence intervals (95 % CI) and 95 % prediction intervals (95%Pr I) as well as statistical heterogeneity data (τ 2, I 2, Q-statistic, and p-value). I 2 values <25 %, ≥25 %–<75 %, and ≥75 % indicated low, moderate, and high heterogeneity respectively (Higgins et al., 2003). Publication bias was assessed by visual inspection of the study prevalence effect estimates on the funnel plot and the Peter's methods (Peters et al., 2006). Peter's technique is based on weighted linear regression on the inverse of the single proportion of the total sample, where a p-value <0.1 shows the presence of publication bias.
To investigate the sources of heterogeneity, subgroup and meta-regression analyses were conducted (Egger et al., 1998; Thompson and Higgins, 2002). In the subgroup and meta-regression analysis, variables (1) gender (male and female); (2) marital status (unmarried and married); (3) study period (Alpha variant dominant period included studies conducted from January to December 2020 and Delta variant dominant period included studies conducted from January to November 2021); (4) countries geographical area (Asia, Africa, Europe, North America, and South America); (5) resilience measurement instruments (CD-RISC 25, CD-RISC 10, the Resilience Scale 25, the Resilience Scale 10, the BRCS, and BRS); (6) study risk of bias (high, moderate and low risk); (7) COVID-19 frontline health professionals (yes and no); and (8) education (bachelor or below and master or above) were used (Table 2 ). The random-effect model results were adopted for the estimated pooled effect estimate in subgroups (Serghiou and Goodman, 2019). To evaluate the robustness of the study's findings, sensitivity analyses were done. First, we removed studies with high risk of bias based on the quality of the studies. Second, we excluded the studies with <100 participants (Yunitri et al., 2022).
Table 2.
Global, subgroup, and random effect meta-regression analysis of low resilience and its associated factors.
| Variables | k (sample size) | Subgroup analysis |
Meta-regression analysis |
||||
|---|---|---|---|---|---|---|---|
| Pooled prevalence of low resilience (%, 95 % CI) | I2 (%) | Q-value | p-Value | Pooled estimate % (95 % CI) | p-Value | ||
| Global analysis | 44 (51,119) | 27.0 (21.0 to 33.0) | 99.0 | ||||
| Participants' characteristics | |||||||
| Population | |||||||
| General population | 16 (34,995) | 35.0 (28.0 to 42.0) | 99.5 | 2905.9 | 0.10 | Ref. | |
| Health professionals | 28 (16,124) | 23.0 (16.0 to 30.9) | 98.5 | 1778.2 | −9.2 (−20.2 to 1.8) | 0.1 | |
| Age | 31 (39,585) | −1.9 (−6.2 to 2.2) | 0.3 | ||||
| Gender | |||||||
| Male | 44 (17,391) | 28.1 (23.7 to 32.9) | 93.8 | 694.1 | <0.01 | Ref. | |
| Female | 44 (35,118) | 71.6 (66.8 to 76.1) | 93.9 | 703.3 | 41.3 (34.7 to 47.8) | <0.01 | |
| Marital status | |||||||
| Unmarried | 33 (19,634) | 10.0 (7.3 to 13.5) | 98.3 | 1903.7 | 0.20 | Ref. | |
| Married | 33 (23,392) | 13.1 (9.0 to 18.8) | 98.4 | 1950.9 | 4.9 (0.7 to 10.5) | 0.8 | |
| Study period | |||||||
| Alpha dominant | 34 (48,281) | 22.9 (17.7 to 29.2) | 99.3 | 4712.9 | 0.02 | Ref. | |
| Delta dominant | 8 (2671) | 41.4 (27.1 to 57.4) | 96.1 | 181.2 | 16.5 (3.4 to 29.6) | 0.01 | |
| Countries geographical area | |||||||
| Asia | 11 (2237) | 31.5 (20.7 to 44.8) | 97.4 | 389.1 | 0.04 | Ref | |
| Africa | 6 (4793) | 38.4 (24.7 to 54.2) | 98.9 | 462.2 | 5.7 (−11.2 to 22.7) | 0.51 | |
| Europe | 19 (32,134) | 20.2 (13.4 to 29.3) | 99.1 | 2063.5 | −9.2 (−21.9 to 3.4) | 0.16 | |
| North America | 5 (2035) | 22.9 (15.1 to 33.2) | 98.8 | 342.6 | −9.7 (−28.3 to 9.8) | 0.30 | |
| South America | 3 (2132) | 45.6 (29.9 to 62.1) | 98.8 | 163.2 | 12.2 (−9.5 to 33.9) | 0.28 | |
| Resilience measurement instruments | |||||||
| CD-RISC 25 | 7 (1945) | 29.4 (16.8 to 46.2) | 97.7 | 265.6 | <0.01 | Ref | |
| CD-RISC 10 | 7 (24,780) | 39.5 (32.4 to 47.0) | 98.6 | 417.3 | 8.6 (−6.8 to 24.1) | 0.27 | |
| The Resilience Scale 25 | 3 (2799) | 20.7 (19.2 to 22.3) | 46.4 | 5.6 | −12.7 (−30.7 to 5.2) | 0.16 | |
| The Resilience Scale 14 | 9 (4557) | 11.9 (5.9 to 22.3) | 98.3 | 471.3 | −15.0 (−29.4 to −0.6) | 0.04 | |
| BRCS | 9 (5672) | 47.8 (37.5 to 58.2) | 98.4 | 501.9 | 16.5 (2.0 to 31.0) | 0.02 | |
| BRS | 7 (11,113) | 20.9 (15.3 to 28.0) | 98.4 | 377.2 | −9.7 (−25.0 to −0.5) | 0.21 | |
| Study risk of bias | |||||||
| High risk | 8 (2248) | 22.7 (18.3 to 27.9) | 76.7 | 30.1 | 0.02 | Ref. | |
| Moderate risk | 27 (19,478) | 22.6 (16.6 to 30.1) | 97.9 | 1418.9 | −1.7 (−72.3 to 69.7) | 0.96 | |
| Low risk | 9 (29,393) | 37.1 (27.7 to 49.1) | 99.1 | 1140.6 | 70.9 (−14.3 to 1.6) | 0.10 | |
| Health professionals | |||||||
| Frontline health professionals | |||||||
| No | 18 (1706) | 6.2 (1.2 to 27.6) | 96.4 | 478.0 | <0.01 | Ref. | |
| Yes | 18 (1721) | 93.6 (7.2 to 98.8) | 96.4 | 478.0 | +49.8 (30 to 69) | <0.01 | |
| Education | |||||||
| Bachelor or below | 11 (4147) | 20.0 (10.4 to 34.9) | 98.9 | 944.3 | <0.01 | Ref. | |
| Master or above | 11 (2028) | 4.0 (1.3 to 11.1) | 98.5 | 676.1 | −14.7 (−31.7 to −2.3) | 0.09 | |
Abbreviation: k: study size, CI: confidence interval, I2: heterogeneity, CD-RISC: Connor Davidson Resilience Scale; BRCS: Brief Resilience Coping Scale; BRS: Brief Resilience Scale.
Bold data indicates p < 0.05.
2.7. Validated resilience measurement tools
Prevalence of low resilience measured by validated instruments including (1) two versions of Connor Davidson Resilience Scale (CD-RISC) that are CD-RISC 25 and CD-RISC 10 with Cronbach alpha ranging from 0.81 to 0.93 (Campbell-Sills and Stein, 2007; Connor and Davidson, 2003), and test-retest reliability ranging from 0.84 to 0.93 (Pretorius, 2021; Waddimba et al., 2022); (2) Brief Resilience Scale (BRS) with Cronbach alpha ranging from 0.71 to 0.85 (Smith et al., 2008), and test-retest reliability of 0.75 (Pretorius, 2021); (3) The Resilience Scale 25 and the Resilience Scale 14 with Cronbach alpha ranging from 0.79 to 0.96 (Sánchez-Teruel and Robles-Bello, 2015; Wagnild and Young, 1993), and test-retest reliability of 0.84 (Pretorius, 2021), and (4) Brief Resilience Coping Scale (BRCS) with Cronbach alpha ranging from 0.67 to 0.78 (Sinclair and Wallston, 2004) and test-retest reliability ranging from 0.87 to 0.95 (Nochaiwong et al., 2022).
3. Results
3.1. Search result summary
Comprehensive literature search in six electronic databases and grey literature (ProQuest, OPENGREY, and WorldWideScience.org) was performed with a total of 4821 studies retrieved from the search. After duplicate removal, 3545 remaining studies were screened by title and abstract using the previously described eligibility criteria. The remaining of 400 studies were eligible for full-text review, and 363 were excluded because only provided the mean score. Manual searching through the website and reference list of relevant studies identified seven additional studies meeting the inclusion criteria. Finally, 44 studies and a total of 51,119 participants were included in this meta-analysis. The literature identified at each stage of the procedure is summarized in Fig. 1.
3.2. Characteristics of the included studies
The total of 44 studies involving 51,119 participants are covered in this meta-analysis. All the included studies used a cross-sectional study design. Among the eligible studies, 16 studies were conducted in general populations and 28 studies were conducted in the health professional's population. Basic descriptive characteristics of the included studies and low resilience cut-off scores adopted by each article are summarized in Table 1.
3.3. Quality appraisal
All included studies were appraised using the 10-item risk of bias tool developed by Hoy and colleagues specifically for observational studies. The evaluation was undertaken by two raters independently, and the Cohen's Kappa test revealed almost perfect agreement (0.803, p < 0.01). Eight studies have a high risk of bias, 27 studies have a moderate risk of bias, and nine studies have a low risk of bias (Table 1; Supplement 4).
3.4. Prevalence of low resilience
Our results showed that the pooled prevalence of low resilience in the overall population was 27.0 % (95 % CI: 21.0 %–33.0 %) with high heterogeneity across 44 studies ( = 4937.2, p < 0.01, I 2 = 99) (Fig. 2). Regarding the population, the pooled prevalence of low resilience was 35.0 % (95 % CI: 28.0 %–42.0 %) among the general population and 23.0 % (95 % CI: 16.0 % to 30.9 %) among health professionals (Fig. 3). We conduct three monthly trend analyses on the prevalence of low resilience during the COVID-19 pandemic starting on January 2020 to June 2021 among the overall population, general population, and health professionals (Fig. 4). A random effects model was used to combine the prevalence of low resilience in each time period (Supplement 3). The result showed an increasing prevalence of low resilience among the overall population, the general population, and health professionals. Among the overall population, the 3-month trend analyses of the prevalence of low resilience revealed an upward and downward pattern. The prevalence of low resilience was 24 % in January–March 2021, slightly decreasing to 21 % in April–June 2020, and increasing to 34 % in July–September 2020. In October–December 2020, the prevalence of low resilience decreases to 25 % before reaching a peak in January–March 2021 (46 %). Among the general population, the prevalence of low resilience was 21 % in January–March 2020, and increase to 29 % in April–June 2020, then spiked to 46 % in periods of January–March 2021. While in the health professional population, the prevalence of low resilience was 26 % in January–March 2020, decreases to 17 % in April–June 2020, then start to increase to 32 % in July–September 2020. In October–December 2020, the proportions of low resilience were decrease to 25 % before spiking to 45 % in January–March 2021.
Fig. 2.
Forest plot overall prevalence of low resilience.
Fig. 3.
Forest plot prevalence of low resilience based on populations.
Fig. 4.
Trend analyses prevalence of low resilience based on populations.
3.5. Subgroup analysis
Subgroup analyses were conducted for the prevalence of low resilience and results are tabulated in Table 2. The prevalence of low resilience was significantly moderated by gender, study period, country geographical area, type of resilience instrument, role as frontline health professionals, and education. The prevalence of low resilience by gender (p < 0.01) was as follows: male: 28.1 % (95 % CI: 23.7 %–32.9 %) and female: 71.6 % (95 % CI: 66.8 %–76.1 %). The study period significantly affected the prevalence of low resilience. In studies conducted during the Delta variant dominant period, the prevalence of low resilience was 41.4 % (95 % CI: 27.1 %–57.4 %) compared to 22.9 % (95 % CI: 17.7 %–29.2 %) during the Alpha variant dominant period. The prevalence of low resilience was highest in South American studies at 45.6 % (95 % CI: 29.9 %–62.1 %) compared to African, Asian, North American, and European studies at 38.4 %, 31.5 %, 28.0 %, and 20.2 %, respectively (Table 2).
The prevalence of low resilience was significantly influenced by the type of resilience measurement instrument (p < 0.01). The prevalence of low resilience was higher in studies that utilized the BRCS (47.8 %; 95 % CI: 37.5 %–58.2 %), than in studies that utilized the CD-RISC 10, (39.5 %; 95 % CI: 32.4 %–47.0 %); CD-RISC 25 (29.4 %; 95 % CI: 16.8 % - 46.2 %); BRS (20.9 %; 95 % CI: 15.3 %–28.0 %); the Resilience Scale 25, (20.7 %; 95 % CI: 19.2 % - 22.3 %), and the Resilience Scale 14, (11.9 %; 95 % CI: 5.9 % - 22.3 %).
Among health professionals, COVID-19 frontliners had a higher prevalence of low resilience (93.6 %; 95 % CI: 7.2 %–98.8 %), than non-COVID-19 frontline health professionals, (6.2 %; 95 % CI: 1.2 %–27.6 %). In the context of education, those with a bachelor's degree or lower had a higher prevalence of low resilience (20.0 %; 95 % CI: 10.4 %–34.9 %) compared to those with a master's degree and above (4.0 %; 95 % CI: 1.3 % to 11.1 %).
3.6. Meta-regression and sensitivity analyses
Meta-regression showed no significant effects of age, marital status, countries' geographical area, resilience measurement instruments, study risk of bias, and education on the pooled prevalence of low resilience (Table 2). We found significant effects of gender (female), study period (Delta variant dominant period), and role as frontline health professionals (frontline health professionals) on pooled prevalence of low resilience. Sensitivity analyses were conducted according to the study quality and studies that enrolled <100 participants. According to study quality, eight studies with a high risk of bias were removed from the analysis and revealing a 26 % prevalence of low resilience. Based on the studies that enrolled <100 participants, three studies were removed and resulting in a 27 % prevalence of low resilience. The results of the sensitivity analyses showed that the pooled prevalence of low resilience during the COVID-19 pandemic did not differ significantly from the main analysis, which ranged from 21 % to 33 %. Thus, the current results of this meta-analysis can be considered to be robust. Publication bias was performed for all included studies. The results of the regression test using Peter's method with t = −1.24 (p = 0.22) and visual inspection of the funnel plot revealed no evidence of publication bias (Supplement 4).
4. Discussion
4.1. Prevalence of low resilience
This study included 44 published studies, yielding a total of 51,119 from overall populations across 20 countries. The pooled prevalence of low resilience in the overall population was 27 %, suggesting one-fourth of the worldwide population suffered from low resilience during the COVID-19 pandemic. Given that COVID-19 substantially affects interpersonal and community interactions, this result is not unexpected. Losing connections and relationships undoubtedly causes stressful states of loneliness, anxiety, depression, mental problems, health risks, and a host of other concerns that have been detrimental to the individual as well as society at large (Singh and Singh, 2020).
The prevalence of low resilience among the general population was 35 %, which means almost four out of ten individuals in the general population have low resilience during the COVID-19 pandemic. In the general population including civil society, university students, family caregivers, and families of healthcare workers, the COVID-19 pandemic has created unprecedented changes to all aspects of life including work life, home life, and also study life (Venkatesh, 2020). When faced with a difficulty, individuals turn to learn to grasp what is going on and try to adjust themselves and the core aspect of resilience is self-regulation aimed at altering a challenging condition or adapting to a given circumstance (Schwager and Rothermund, 2013). Thus, there is a need for the implementation of resilience-enhancing programs among individuals with a high prevalence of low resilience.
The prevalence of low resilience was 23.0 % among health professionals, but still considerably lower compared to that of the general population. These results may indicate that health professionals have a better and greater capacity to adapt to adversity caused by the COVID-19 pandemic compared to the general population. However, considering the epidemic's intensification, the fact that one-fourth of the population of health professionals has low levels of resilience is highly concerning. Health professionals with low resilience are prone to the negative psychological consequences of working during a pandemic (Baskin and Bartlett, 2021).
Resilience from social-ecological viewpoints remarks that there should be an interaction between individuals and their environments to optimize the development process (Ungar, 2013). Referring to this definition, if individuals do not reach the expected level of resilience (or have low resilience), it is necessary to evaluate the accessibility of available resources. At the social level, the encouragement from existing relationships and social support groups boosts their social identity and improves material and informational resource availability. Thus, governments or health policymakers should enhance healthcare availability and accessibility including vaccinations and other pandemic-related services.
Regarding the trends of low resilience during the COVID-19 pandemic, the overall population, general population, and health professionals showed increasing trends. There were similar upward and downward trends among the overall population (24 % to 33 %) and health professionals (26 % to 45 %) while the general population demonstrated an increasing pattern (21 % to 46 %). Among the overall population, there was a significant decrease in the prevalence of low resilience in the October–December 2020 period. The rationale is that by the end of 2020 the COVID-19 vaccine had been developed and made accessible, which enhanced the resilience of the overall population (Huy et al., 2022). From January to March of 2021, the prevalence of individuals with low resilience increased to 46 % may be attributed to the global spread of Delta variant, which was associated with high rates of morbidity and mortality. According to a prior study, the Delta variant were linked to high levels of anxiety and worry that have been associated with a significant decline in resilience levels (Alhasan et al., 2021; McCrone et al., 2022).
4.2. Significant moderator factors for the pooled prevalence of low resilience
4.2.1. Gender
Gender significantly moderated the pooled prevalence of low resilience during the COVID-19 pandemic. The prevalence of low resilience was higher in females than in males across the overall population (71.8 %). These results are consistent with the findings of a previous meta-analysis, which stated that females have lower resilience compared to males (Ayse and Kogar, 2021). After disasters and severe mass-trauma events, females frequently, but not always, exhibit increased signs of distress, depressed mood, or anxiety (Masten, 2015). Females tend to be more prone to vulnerability and sensitivity, and they may have less developed stress management skills, which may not be enough to support psychological resilience.
4.2.2. The study periods
The study period was a significant moderator variable for the pooled prevalence of low resilience. Studies conducted from January to December 2020 were considered as being in the Alpha variant dominant period, whereas studies conducted from January to November 2021 were considered as being in the Delta variant dominant period. However, the Delta variant was first identified in India in October 2020 and later found in other countries (Roy and Roy, 2021; WHO, 2021). The prevalence of low resilience during the Delta variant dominant period is higher compared to the Alpha variant dominant period (41.4 %). In a prior study, the prevalence of anxiety and worries was increased in the delta variant dominant period, and a subsequent study revealed that anxiety and worries were hindering resilience (Alhasan et al., 2021; Panzeri et al., 2021). Though the COVID-19 pandemic seems to have stabilized, anticipating potential and possible new highly infectious variants that might impact on the adaptation to the COVID-19 adversity should be taken into consideration in practice.
4.2.3. Country geographical area
Considering countries' geographical areas, South America had the highest prevalence of low resilience during the COVID-19 pandemic (45.6 %). South America was severely affected by COVID-19, accounting for about 16 % and 24 % of global cases and deaths, respectively (Musa et al., 2022). High morbidity and mortality rates attributable to COVID-19 have resulted in elevated fear of death, which was associated with low levels of resilience.
4.2.4. Resilience measurement tools
Regarding resilience measuring instruments, the BRCS showed to record the highest prevalence of low resilience (47.8 %), and these results are consistent with the previous study (Cheng et al., 2022). The BRCS consists of 4 items with responses on a 5-point Likert scale and scores summed to generate a single total score with the cut-off point value of low resilience being <13. The key value of the BRCS is that it offers a one-dimensional framework, which is straightforward to implement, but the limited number of items could likely lead to the overestimation of the prevalence estimates of low resilience.
4.2.5. Role as frontline health professional
The role of frontline health professionals during the COVID-19 pandemic was shown to be a significant moderator of the pooled prevalence of low resilience (p < 0.01). Those who worked as COVID-19 frontline health professionals had a high prevalence of low resilience (93.6 %) compared to non-COVID-19 frontline health professionals (6.2 %). Our study results are consistent with those reported by Cai et al. (2020) in which the rate of mental issues, including anxiety, depression, and sleeplessness, was considerably higher among frontline health professionals than among non-frontline health professionals, resulting in a higher prevalence of low resilience.
4.2.6. Educational background of health professional
Health professionals with bachelor's degrees or below had a higher prevalence of low resilience compared with those with master's degrees or above. These findings are consistent with previous studies that found a correlation between education and resilience (Kumar et al., 2022). Higher educational attainment has been shown to enhance the ability to manage stress in challenging situations fostering resilience. Thus, a high level of education attainment leads to improved coping skills, and the development of social skills might be better at separating accurate from inaccurate information and leading to increased psychological resilience (Karasar and Canli, 2020).
4.3. Strength and limitations
This meta-analysis has numerous strengths. First, this study provides comprehensive evidence of the global prevalence of low prevalence during the COVID-19 pandemic among the general population and health professionals. Second, we conducted comprehensive literature searches in electronic databases and grey literature without language restrictions through independent screening, careful data extraction, and rigorous quality assessment. Third, we conducted sensitivity analyses revealing the robustness of the current study findings. Despite the study's strengths, certain limitations must be taken into account when interpreting its findings. First, the study outcomes showed high heterogeneity; however, sub-group and meta-regression analyses were conducted to identify potential moderating factors. Second, according to Sani et al. (2022), coping strategies are important characteristics of resilience for health professionals; however, we were unable to conduct further analysis on the coping strategies used by health professionals due to limited number of studies. Thus, future studies exploring the prevalence of low resilience should be encouraged to examine the coping strategies used by health professionals and general population.
4.4. Implications
This study contributes to the growing knowledge by providing evidence on the prevalence of low resilience among the general population and health professionals during the COVID-19 pandemic. Resilience research aims to provide new perspectives on the widely diverse mental health field trajectories (Stainton et al., 2019). Thus, additional work is required to identify and manage low resilience to prevent mental health issues resulting from pandemics including the COVID-19 pandemic. Encouraging positive social support and implementing resilience training programs that may increase resilience because everyone can be trained to engage in the resilience process. Resilience training is crucial for handling stress and minimizing its negative effects (Gheshlagh et al., 2016). Cognitive behavioral therapy, mindfulness, acceptance and commitment therapy, and positive psychology approaches can be utilized in developing resilience training programs (Kunzler et al., 2022). In addition, developing organizational justice for health professionals may be a way to promote fairness and respect, especially during pandemics when workloads are burdensome (Rieckert et al., 2021).
5. Conclusions
The study results indicate a substantial prevalence of low resilience, with a greater prevalence among the general population than among health professionals. The prevalence of low resilience among the general population was twice as high as among health professionals. Females, COVID-19 frontline health professionals, and those with lower education were found to be less resilient to COVID-19 adversity. Thus, to prevent and mitigate negative mental health issues during the COVID-19 pandemic, it is necessary to provide positive social support for the overall population, especially to the general population, particularly female participants, health professionals with a lower level of education, and health professionals who worked on the frontlines of COVID-19 pandemic.
The following are the supplementary data related to this article.
Search string.
Risk of bias of included studies.
Forest plot prevalence of low resilience over time.
Funnel plot of publication bias.
PRISMA Checklist
MOOSE Checklist
Funding source
This research did not receive any specific grant from public, commercial, or non-profit funding agencies.
CRediT authorship contribution statement
Fitria Endah Janitra: Data curation, Formal analysis, Software, Visualization, Writing – original draft. Hsiu-Ju Jen: Software, Validation. Hsin Chu: Software, Validation. Ruey Chen: Software, Validation. Li-Chung Pien: Software, Validation. Doresses Liu: Software, Validation. Yueh-Jung Lai: Software, Validation. Kondwani Joseph Banda: Software, Validation, Writing – review & editing. Tso-Ying Lee: Software, Validation. Hui-Chen Lin: Software, Validation. Ching-Yi Chang: Software, Validation. Kuei-Ru Chou: Conceptualization, Supervision, Validation, Writing – review & editing.
Conflict of interest
Authors declare no conflict of interest.
Acknowledgments
The findings and conclusions in this document are those of the authors, who are responsible for its contents.
Data availability
As this is a meta-analysis of previous data, no new data were collected to support this study.
References
- Alameddine M., Bou-Karroum K., Ghalayini W., Abiad F. Resilience of nurses at the epicenter of the COVID-19 pandemic in Lebanon. Int. J. Nurs. Sci. 2021;8:432–438. doi: 10.1016/j.ijnss.2021.08.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Alameddine M., Clinton M., Bou-Karroum K., Richa N., Doumit M.A.A. Factors associated with the resilience of nurses during the COVID-19 pandemic. Worldviews Evid.-Based Nurs. 2021;18:320–331. doi: 10.1111/wvn.12544. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Aldhahi M.I., Akil S., Zaidi U., Mortada E., Awad S., Al Awaji N. Effect of resilience on health-related quality of life during the COVID-19 pandemic: a cross-sectional study. Int. J. Environ. Res. Public Health. 2021;18 doi: 10.3390/ijerph182111394. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Alhasan K., Aljamaan F., Temsah M.-H., Alshahrani F., Bassrawi R., Alhaboob A., Assiri R., Alenezi S., Alaraj A., Alhomoudi R.I., Batais M.A., Al-Eyadhy L., Halwani R., AbdulMajeed N., Al-Jedai A., Senjab A., Memish Z.A., Al-Subaie S., Barry M., Al-Tawfiq J.A. COVID-19 Delta variant: perceptions, worries, and vaccine-booster acceptability among healthcare workers. Healthcare. 2021;9:1566. doi: 10.3390/healthcare9111566. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Antonijevic J., Binic I., Zikic O., Manojlovic S., Tosic-Golubovic S., Popovic N. Mental health of medical personnel during the COVID-19 pandemic. Brain Behav. 2020;10 doi: 10.1002/brb3.1881. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ayse G., Kogar E.Y. A meta-analysis study on gender differences in psychological resilience levels. Kıbrıs Türk Psikiyatri ve Psikoloji Dergisi. 2021;3:132–143. [Google Scholar]
- Baptista S., Teixeira A., Castro L., Cunha M., Serrão C., Rodrigues A., Duarte I. Physician burnout in primary care during the COVID-19 pandemic: a cross-sectional study in Portugal. J. Prim. Care Community Health. 2021:1–9. doi: 10.1177/21501327211008437. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Baskin R.G., Bartlett R. Healthcare worker resilience during the COVID-19 pandemic: An integrative review. J. Nurs. Manag. 2021;29:2329–2342. doi: 10.1111/jonm.13395. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bates A., Ottaway J., Moyses H., Perrrow M., Rushbrook S., Cusack R. Psychological impact of caring for critically ill patients during the Covid-19 pandemic and recommendations for staff support. J.Intensive Care Soc. 2021;22:312–318. doi: 10.1177/1751143720965109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Borges G., Arias S.D., Carmona-Huerta J., Aldana-López A., Náfate-López O., Orozco R., Cordoba G., Alvarado R. 2022. Risk Factors and Resilience Associated to Depressive Symptoms in Mexican Healthcare Workers During the Early Stages of the Covid-19 Pandemic. [DOI] [PubMed] [Google Scholar]
- Cai W., Lian B., Song X., Hou T., Deng G., Li H. A cross-sectional study on mental health among health care workers during the outbreak of Corona Virus Disease 2019. Asian J. Psychiatr. 2020;51 doi: 10.1016/j.ajp.2020.102111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Campbell-Sills L., Stein M.B. Psychometric analysis and refinement of the Connor–Davidson Resilience Scale (CD-RISC): validation of a 10-item measure of resilience. J. Trauma. Stress. 2007;20:1019–1028. doi: 10.1002/jts.20271. [DOI] [PubMed] [Google Scholar]
- Cheng C.K.T., Chua J.H., Cheng L.J., Ang W.H.D., Lau Y. Global prevalence of resilience in health care professionals: a systematic review, meta-analysis and meta-regression. J. Nurs. Manag. 2022;30:795–816. doi: 10.1111/jonm.13558. [DOI] [PubMed] [Google Scholar]
- Connor K.M., Davidson J.R. Development of a new resilience scale: the Connor-Davidson Resilience Scale (CD-RISC) Depress Anxiety. 2003;18:76–82. doi: 10.1002/da.10113. [DOI] [PubMed] [Google Scholar]
- da Silva Pigati P.A., Righetti R.F., Nisiaymamoto B.T.C., Saraiva-Romanholo B.M. Resilience and its impact on the mental health of physiotherapists during the COVID-19 pandemic in São Paulo, Brazil. J. Affect. Disord. 2022;310:422–428. doi: 10.1016/j.jad.2022.05.049. [DOI] [PMC free article] [PubMed] [Google Scholar]
- de-Torres García I., Bustos F., Arango-Lasprilla J.C., Fernández-Berrocal P. Emotional intelligence abilities of caregivers of patients with acquired brain injury and cognitive impairment: are they related to overload or resilience? Ansiedad y Estres. 2021;28:55–61. [Google Scholar]
- Duarte I., Teixeira A., Castro L., Marina S., Ribeiro C., Jácome C., Martins V., Ribeiro-Vaz I., Pinheiro H.C., Silva A.R., Ricou M., Sousa B., Alves C., Oliveira A., Silva P., Nunes R., Serrão C. Burnout among portuguese healthcare workers during the COVID-19 pandemic. BMC Public Health. 2020;20:1885. doi: 10.1186/s12889-020-09980-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Egger M., Schneider M., Smith G.D. Meta-analysis spurious precision? Meta-analysis of observational studies. BMJ. 1998;316:140–144. doi: 10.1136/bmj.316.7125.140. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Faulkner J.-P., Murphy E., Scott M. Developing a holistic ‘vulnerability-resilience’ model for local and regional development. Eur. Plan. Stud. 2020;28:2330–2347. [Google Scholar]
- Forycka J., Pawłowicz-Szlarska E., Burczyńska A., Cegielska N., Harendarz K., Nowicki M. Polish medical students facing the pandemic-assessment of resilience, well-being and burnout in the COVID-19 era. PLoS One. 2022;17 doi: 10.1371/journal.pone.0261652. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Franck E., Goossens E., Haegdorens F., Geuens N., Portzky M., Tytens T., Dilles T., Beeckman K., Timmermans O., Slootmans S., Van Rompaey B., Van Bogaert P. Role of resilience in healthcare workers' distress and somatization during the COVID-19 pandemic: a cross-sectional study across Flanders, Belgium. Nurs.Open. 2022;9:1181–1189. doi: 10.1002/nop2.1159. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ghahramani S., Kasraei H., Hayati R., Tabrizi R., Marzaleh M.A. Health care workers' mental health in the face of COVID-19: a systematic review and meta-analysis. Int. J. Psychiatry Clin. Pract. 2022:1–10. doi: 10.1080/13651501.2022.2101927. [DOI] [PubMed] [Google Scholar]
- Gheshlagh R.G., Sayehmiri K., Ebadi A., Dalvandi A., Dalvand S., Tabrizi K.N. Resilience of patients with chronic physical diseases: a systematic review and meta-analysis. Iran. Red Crescent Med. J. 2016;18:9. doi: 10.5812/ircmj.38562. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Higgins J.P., Thompson S.G., Deeks J.J., Altman D.G. Measuring inconsistency in meta-analyses. Bmj. 2003;327:557–560. doi: 10.1136/bmj.327.7414.557. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Horn S.R., Feder A. Understanding resilience and preventing and treating PTSD. Harv.Rev.Psychiatry. 2018;26:158–174. doi: 10.1097/HRP.0000000000000194. [DOI] [PubMed] [Google Scholar]
- Hoy D., Brooks P., Woolf A., Blyth F., March L., Bain C., Baker P., Smith E., Buchbinder R. Assessing risk of bias in prevalence studies: modification of an existing tool and evidence of interrater agreement. J. Clin. Epidemiol. 2012;65:934–939. doi: 10.1016/j.jclinepi.2011.11.014. [DOI] [PubMed] [Google Scholar]
- Huang L., Wang Y., Liu J., Ye P., Chen X., Xu H., Qu H., Ning G. Factors influencing anxiety of health care workers in the radiology department with high exposure risk to COVID-19. Med. Sci. Monit. 2020;26 doi: 10.12659/MSM.926008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huy L.D., Shih C.L., Chang Y.M., Nguyen N.T.H., Phuc P.T., Ou T.Y., Huang C.C. Comparison of COVID-19 resilience index and its associated factors across 29 countries during the Delta and Omicron variant periods. Vaccines (Basel) 2022:10. doi: 10.3390/vaccines10060940. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ionescu T.C., Fetecau B.I., Boscaiu V., Tudose C. Anxiety, perceived stress, and resilience during the Covid-19 pandemic: population estimates of persons presenting to a general practitioner in Romania. Brain Sci. 2021;11 doi: 10.3390/brainsci11111541. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jácome C., Seixas A., Serrão C., Teixeira A., Castro L., Duarte I. Burnout in Portuguese physiotherapists during COVID-19 pandemic. Physiother. Res. Int. 2021;26:1–10. doi: 10.1002/pri.1915. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jose S., Dhandapani M., Cyriac M.C. Burnout and resilience among frontline nurses during Covid-19 pandemic: a cross-sectional study in the emergency department of a tertiary care center, North India. Indian J.Crit.Care Med. 2020;24:1081–1088. doi: 10.5005/jp-journals-10071-23667. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Karasar B., Canli D. Psychological resilience and depression during the Covid-19 pandemic in Turkey. Psychiatr. Danub. 2020;32:273–279. doi: 10.24869/psyd.2020.273. [DOI] [PubMed] [Google Scholar]
- Kelker H., Yoder K., Musey P., Harris M., Johnson O., Sarmiento E., Vyas P., Henderson B., Adams Z., Welch J. Prospective study of emergency medicine provider wellness across ten academic and community hospitals during the initial surge of the COVID-19 pandemic. BMC Emerg.Med. 2021;21 doi: 10.1186/s12873-021-00425-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Khalaf O.O., Khalil M.A., Abdelmaksoud R. Coping with depression and anxiety in Egyptian physicians during COVID-19 pandemic. Middle East Curr.Psychiatry. 2020;27:63. [Google Scholar]
- Konlan K.D., Asampong E., Dako-Gyeke P., Glozah F.N. Burnout syndrome among healthcare workers during COVID-19 pandemic in Accra,Ghana. PloS one. 2022;17 doi: 10.1371/journal.pone.0268404. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kumar S., Lee N.K., Pinkerton E., Wroblewski K.E., Lengyel E., Tobin M. Resilience: a mediator of the negative effects of pandemic-related stress on women's mental health in the USA. Arch.Women's Ment.Health. 2022;25:137–146. doi: 10.1007/s00737-021-01184-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kunzler A.M., Chmitorz A., Röthke N., Staginnus M., Schäfer S.K., Stoffers-Winterling J., Lieb K. Interventions to foster resilience in nursing staff: a systematic review and meta-analyses of pre-pandemic evidence. Int. J. Nurs. Stud. 2022;134 doi: 10.1016/j.ijnurstu.2022.104312. [DOI] [PubMed] [Google Scholar]
- Lara-Cabrera M.L., Betancort M., Muñoz-Rubilar C.A., Novo N.R., De las Cuevas C. The mediating role of resilience in the relationship between perceived stress and mental health. Int. J. Environ. Res. Public Health. 2021:18. doi: 10.3390/ijerph18189762. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lin L., Chu H. Meta-analysis of proportions using generalized linear mixed models. Epidemiology. 2020;31:713–717. doi: 10.1097/EDE.0000000000001232. [DOI] [PMC free article] [PubMed] [Google Scholar]
- LoGiudice J.A., Bartos S. Experiences of nurses during the COVID-19 pandemic: a mixed-methods study. AACN Adv.Crit.Care. 2021;32:14–25. doi: 10.4037/aacnacc2021816. [DOI] [PubMed] [Google Scholar]
- Lucena J.G.S.d., Azevedo T.F.d., Matias L.D.M., Nunes W.d.B., Costa M.M.L., Andrade L.L.d. Factors associated with resilience in people with diabetes during the social distancing period imposed by the covid-19 pandemic. Texto & Contexto-Enfermagem. 2022:31. [Google Scholar]
- Lundorff M., Holmgren H., Zachariae R., Farver-Vestergaard I., O'Connor M. Prevalence of prolonged grief disorder in adult bereavement: a systematic review and meta-analysis. J. Affect. Disord. 2017;212:138–149. doi: 10.1016/j.jad.2017.01.030. [DOI] [PubMed] [Google Scholar]
- Luthar S.S., Lyman E.L., Crossman E.J. In: Handbook of Developmental Psychopathology. Lewis M., Rudolph K.D., editors. SpringerUS; Boston, MA: 2014. Resilience and positive psychology; pp. 125–140. [Google Scholar]
- Manzanares I., Guerra S.S., Mencia M.L., Acar-Denizli N., Salmeron J.M., Estalella G.M. Impact of the COVID-19 pandemic on stress, resilience and depression in health professionals: a cross-sectional study. Int. Nurs. Rev. 2021;68:461–470. doi: 10.1111/inr.12693. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Masten A.S. Guilford Publications; 2015. Ordinary Magic: Resilience in Development. [Google Scholar]
- McCrone J.T., Hill V., Bajaj S., Pena R.E., Lambert B.C., Inward R., Bhatt S., Volz E., Ruis C., Dellicour S., Baele G., Zarebski A.E., Sadilek A., Wu N., Schneider A., Ji X., Raghwani J., Jackson B., Colquhoun R., O’Toole Á., Peacock T.P., Twohig K., Thelwall S., Dabrera G., Myers R., Faria N.R., Huber C., Bogoch I.I., Khan K., du Plessis L., Barrett J.C., Aanensen D.M., Barclay W.S., Chand M., Connor T., Loman N.J., Suchard M.A., Pybus O.G., Rambaut A., Kraemer M.U.G., The C.-G.U.K.C. Context-specific emergence and growth of the SARS-CoV-2 Delta variant. Nature. 2022;610:154–160. doi: 10.1038/s41586-022-05200-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- McHugh M.L. Interrater reliability: the kappa statistic. Biochem. Med. (Zagreb) 2012;22:276–282. [PMC free article] [PubMed] [Google Scholar]
- Meda-Lara R.M., Muñoz-Valle J.F., Juárez-Rodríguez P., Figueroa-López C., Herrero M., de Santos Ávila F., Palomera-Chávez A., Yeo Ayala C., Moreno-Jiménez B. Psychological responses to COVID-19 in a mexican population: an exploratory study during second and third phases. Psychol. Health Med. 2022;27:257–264. doi: 10.1080/13548506.2021.1883689. [DOI] [PubMed] [Google Scholar]
- Musa S.S., Tariq A., Yuan L., Haozhen W., He D. Infection fatality rate and infection attack rate of COVID-19 in South American countries. Infect.Dis.Poverty. 2022;11:40. doi: 10.1186/s40249-022-00961-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Nochaiwong S., Ruengorn C., Awiphan R., Phosuya C., Ruanta Y., Kanjanarat P., Wongpakaran N., Wongpakaran T., Thavorn K. Transcultural adaptation and psychometric validation of the Thai-brief resilient coping scale: a cross-sectional study during the coronavirus disease 2019 pandemic in Thailand. Sci. Rep. 2022;12:21521. doi: 10.1038/s41598-022-26063-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Panzeri A., Bertamini M., Butter S., Levita L., Gibson-Miller J., Vidotto G., Bentall R.P., Bennett K.M. Factors impacting resilience as a result of exposure to COVID-19: the ecological resilience model. PLoS One. 2021;16 doi: 10.1371/journal.pone.0256041. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Pappa S., Barnett J., Berges I., Sakkas N. Tired, worried and burned out, but still resilient: a cross-sectional study of mental health workers in the UK during the covid-19 pandemic. Int. J. Environ. Res. Public Health. 2021;18 doi: 10.3390/ijerph18094457. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Penacoba C., Catala P., Velasco L., Carmona-Monge F.J., Garcia-Hedrera F.J., Gil-Almagro F. Stress and quality of life of intensive care nurses during the COVID-19 pandemic: self-efficacy and resilience as resources. Nurs.Crit.Care. 2021;26:493–500. doi: 10.1111/nicc.12690. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Peters J.L., Sutton A.J., Jones D.R., Abrams K.R., Rushton L. Comparison of two methods to detect publication bias in meta-analysis. JAMA. 2006;295:676–680. doi: 10.1001/jama.295.6.676. [DOI] [PubMed] [Google Scholar]
- Pinho R., Costa T., Silva N., Areal A., Salles A., Oliveira A., Rassi C., Gomes C., da Silva D., de Oliveira F. 2022. High Prevalence of Burnout Syndrome Among Medical and Nonmedical Residents During the COVID-19 Pandemic (preprint) [DOI] [PMC free article] [PubMed] [Google Scholar]
- Pretorius T.L. Depression among health care students in the time of COVID-19: the mediating role of resilience in the hopelessness-depression relationship. South Afr.J.Psychol. 2021;51:269–278. [Google Scholar]
- Quintiliani L., Sisto A., Vicinanza F., Curcio G., Tambone V. Resilience and psychological impact on Italian university students during COVID-19 pandemic. Distance learning and health. Psychol. Health Med. 2022;27:69–80. doi: 10.1080/13548506.2021.1891266. [DOI] [PubMed] [Google Scholar]
- R-CoreTeam . R Foundation for Statistical Computing; Vienna, Austria: 2022. R: A Language and Environment for Statistical Computing. [Google Scholar]
- Rieckert A., Schuit E., Bleijenberg N., Ten Cate D., De Lange W., de Man-van Ginkel J.M., Mathijssen E., Smit L.C., Stalpers D., Schoonhoven L. How can we build and maintain the resilience of our health care professionals during COVID-19? Recommendations based on a scoping review. BMJ Open. 2021;11 doi: 10.1136/bmjopen-2020-043718. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Riehm K.E., Brenneke S.G., Adams L.B., Gilan D., Lieb K., Kunzler A.M., Smail E.J., Holingue C., Stuart E.A., Kalb L.G., Thrul J. Association between psychological resilience and changes in mental distress during the COVID-19 pandemic. J. Affect. Disord. 2021;282:381–385. doi: 10.1016/j.jad.2020.12.071. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Roberts N.J., McAloney-Kocaman K., Lippiett K., Ray E., Welch L., Kelly C. Levels of resilience, anxiety and depression in nurses working in respiratory clinical areas during the COVID pandemic. Respir. Med. 2021;176 doi: 10.1016/j.rmed.2020.106219. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Román-Mata S.S., Zurita-Ortega F., Puertas-Molero P., Badicu G., González-Valero G. A predictive study of resilience and its relationship with academic and work dimensions during the COVID-19 pandemic. J. Clin. Med. 2020;9:1–11. doi: 10.3390/jcm9103258. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Roy B., Roy H. The Delta Plus variant of COVID-19: will it be the worst nightmare in the SARS-CoV-2 pandemic? J.Biomed.Sci. 2021;8:1–2. [Google Scholar]
- Sabrina F., Chowdhury M.T.H., Nath S.K., Imon A.A., Quader S.M.A., Jahan M.S., Noor A.E., Podder C.P., Gainju U., Niroula R., Rahman M.A. Psychological distress among Bangladeshi dental students during the COVID-19 pandemic. Int. J. Environ. Res. Public Health. 2021;19 doi: 10.3390/ijerph19010176. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sachdeva A., Kumar V., Nandini H., Shaan F. Psychological impact of COVID-19 patient care on families of healthcare workers. Iran. J. Psychiatry. 2022;17:217–223. doi: 10.18502/ijps.v17i2.8912. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sakr C.J., Rahme D., Fakih L., Assaf S.A., Redlich C.A., Slade M.D., Fakhreddine M., Usta J., Musharrafieh U., Maalouf G., Khater B. Anxiety among healthcare workers during COVID-19 pandemic in Lebanon: the importance of the work environment and personal resilience. Psychol. Res. Behav. Manag. 2022;15:811–821. doi: 10.2147/PRBM.S350125. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Salari N., Hosseinian-Far A., Jalali R., Vaisi-Raygani A., Rasoulpoor S., Mohammadi M., Rasoulpoor S., Khaledi-Paveh B. Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. Glob. Health. 2020;16:1–11. doi: 10.1186/s12992-020-00589-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sampogna G., Del Vecchio V., Giallonardo V., Luciano M., Albert U., Carmassi C., Carrà G., Cirulli F., Dell'osso B., Menculini G., Nanni M., Pompili M., Sani G., Volpe U., Bianchini V., Fiorillo A. What is the role of resilience and coping strategies on the mental health of the general population during the covid-19 pandemic? Results from the italian multicentric comet study. Brain Sci. 2021:11. doi: 10.3390/brainsci11091231. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sánchez-Teruel D., Robles-Bello M.A. Escala de resiliencia 14 ítems (RS-14): propiedades psicométricas de la versión en español. Revista Iberoamericana de Diagnóstico y Evaluación-e Avaliação Psicológica. 2015;2:103–113. [Google Scholar]
- Sani G., Janiri D., Moccia L., Albert U., Carrà G., Carmassi C., Cirulli F., Dell'Osso B., Menculini G., Nanni M.G., Pompili M., Volpe U., Fiorillo A. Psychopathological burden and coping strategies among frontline and second-line Italian healthcare workers facing the COVID-19 emergency: findings from the COMET collaborative network. J. Affect. Disord. 2022;311:78–83. doi: 10.1016/j.jad.2022.05.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schwager S., Rothermund K. The Automatic Basis of Resilience. The Resilience Handbook: Approaches to Stress and Trauma. 2013. pp. 55–72. [Google Scholar]
- Sehsah R., Gaballah M.H., El-Gilany A.-H., Albadry A.A. Psychological distress among Egyptian physicians during COVID-19 pandemic. Int. Arch. Occup. Environ. Health. 2021;94:731–740. doi: 10.1007/s00420-020-01624-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Serghiou S., Goodman S.N. Random-effects meta-analysis: summarizing evidence with caveats. JAMA. 2019;321:301–302. doi: 10.1001/jama.2018.19684. [DOI] [PubMed] [Google Scholar]
- Serrano S.A., Sanz Ponce R., González Bertolín A. Resilience and covid-19. An analysis in university students during confinement. Educ. Sci. 2021:11. [Google Scholar]
- Sinclair V.G., Wallston K.A. The development and psychometric evaluation of the Brief Resilient Coping Scale. Assessment. 2004;11:94–101. doi: 10.1177/1073191103258144. [DOI] [PubMed] [Google Scholar]
- Singh J., Singh J. COVID-19 and its impact on society. Electron.Res.J.Soc.Sci.Humanit. 2020;2 [Google Scholar]
- Sińska B., Jaworski M., Panczyk M., Traczyk I., Kucharska A. The role of resilience and basic hope in the adherence to dietary recommendations in the Polish population during the COVID-19 pandemic. Nutrients. 2021;13 doi: 10.3390/nu13062108. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Smith B.W., Dalen J., Wiggins K., Tooley E., Christopher P., Bernard J. The brief resilience scale: assessing the ability to bounce back. Int.J.Behav.Med. 2008;15:194–200. doi: 10.1080/10705500802222972. [DOI] [PubMed] [Google Scholar]
- Stainton A., Chisholm K., Kaiser N., Rosen M., Upthegrove R., Ruhrmann S., Wood S.J. Resilience as a multimodal dynamic process. Early Interv.Psychiatry. 2019;13:725–732. doi: 10.1111/eip.12726. [DOI] [PubMed] [Google Scholar]
- Stocchetti N., Segre G., Zanier E.R., Zanetti M., Campi R., Scarpellini F., Clavenna A., Bonati M. Burnout in intensive care unit workers during the second wave of the covid-19 pandemic: a single center cross-sectional italian study. Int. J. Environ. Res. Public Health. 2021;18 doi: 10.3390/ijerph18116102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Thompson S.G., Higgins J.P. How should meta-regression analyses be undertaken and interpreted? Stat. Med. 2002;21:1559–1573. doi: 10.1002/sim.1187. [DOI] [PubMed] [Google Scholar]
- Tsehay M., Belete A., Necho M. Factors associated with psychological distress and brief resilient coping level during the COVID-19 pandemic among health-care professionals in Dessie, Ethiopia. Psychol. Res. Behav. Manag. 2020;13:1213–1221. doi: 10.2147/PRBM.S288562. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ungar M. Resilience, trauma, context, and culture. Trauma Violence Abuse. 2013;14:255–266. doi: 10.1177/1524838013487805. [DOI] [PubMed] [Google Scholar]
- Venkatesh V. Impacts of COVID-19: a research agenda to support people in their fight. Int. J. Inf. Manag. 2020;55 doi: 10.1016/j.ijinfomgt.2020.102197. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Verdolini N., Amoretti S., Montejo L., García-Rizo C., Hogg B., Mezquida G., Rabelo-da-Ponte F.D., Vallespir C., Radua J., Martinez-Aran A., Pacchiarotti I., Rosa A.R., Bernardo M., Vieta E., Torrent C., Solé B. Resilience and mental health during the COVID-19 pandemic. J. Affect. Disord. 2021;283:156–164. doi: 10.1016/j.jad.2021.01.055. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Waddimba A.C., Baker B.M., Pogue J.R., McAuliffe M.P., Bennett M.M., Baxter R.D., Mohr D.C., Warren A.M. Psychometric validity and reliability of the 10- and 2-item Connor-Davidson resilience scales among a national sample of Americans responding to the Covid-19 pandemic: an item response theory analysis. Qual. Life Res. 2022;31:2819–2836. doi: 10.1007/s11136-022-03125-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wagnild G.M., Young H.M. Development and psychometric evaluation of the resilience scale. J. Nurs. Meas. 1993;1:165–178. [PubMed] [Google Scholar]
- Weitzel E.C., Löbner M., Röhr S., Pabst A., Reininghaus U., Riedel-Heller S.G. Prevalence of high resilience in old age and association with perceived threat of covid-19—results from a representative survey. Int. J. Environ. Res. Public Health. 2021;18 doi: 10.3390/ijerph18137173. [DOI] [PMC free article] [PubMed] [Google Scholar]
- WHO . World Health Organization; 2021. Tracking SARS-CoV-2 Variants. [Google Scholar]
- Wu T., Jia X., Shi H., Niu J., Yin X., Xie J., Wang X. Prevalence of mental health problems during the COVID-19 pandemic: a systematic review and meta-analysis. J. Affect. Disord. 2021;281:91–98. doi: 10.1016/j.jad.2020.11.117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Xiong J., Lipsitz O., Nasri F., Lui L.M.W., Gill H., Phan L., Chen-Li D., Iacobucci M., Ho R., Majeed A., McIntyre R.S. Impact of COVID-19 pandemic on mental health in the general population: a systematic review. J. Affect. Disord. 2020;277:55–64. doi: 10.1016/j.jad.2020.08.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yunitri N., Chu H., Kang X.L., Jen H.J., Pien L.C., Tsai H.T., Kamil A.R., Chou K.R. Global prevalence and associated risk factors of posttraumatic stress disorder during COVID-19 pandemic: a meta-analysis. Int. J. Nurs. Stud. 2022;126 doi: 10.1016/j.ijnurstu.2021.104136. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zakeri M.A., Hossini Rafsanjanipoor S.M., Zakeri M., Dehghan M. The relationship between frontline nurses' psychosocial status, satisfaction with life and resilience during the prevalence of COVID-19 disease. Nurs.Open. 2021;8:1829–1839. doi: 10.1002/nop2.832. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhang R., Lai J., Wang Y., Huang J., Hu S., Wang H. Mental health outcome and resilience among aiding Wuhan nurses: one year after the COVID-19 outbreak in China. J. Affect. Disord. 2022;297:348–352. doi: 10.1016/j.jad.2021.10.050. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Search string.
Risk of bias of included studies.
Forest plot prevalence of low resilience over time.
Funnel plot of publication bias.
PRISMA Checklist
MOOSE Checklist
Data Availability Statement
As this is a meta-analysis of previous data, no new data were collected to support this study.