Abstract
Background
Studies on protective factors of suicidal risk are limited. Resilience has gained importance as a factor associated with psychological distress. However, the relationship between psychosocial factors, resilience, and suicidality has not been extensively explored among the general public. The study aimed to understand the relationship between psychological distress, suicide risks, and resilience.
Methods
A cross-sectional study was conducted to examine a representative sample of the Taiwanese general population. The population-based survey investigated the associations between resilience, psychological distress, and suicide-related features in 2017. The measurements included self-rated health, a 5-item Brief Symptom Rating Scale, a 9-item Concise Mental Health Checklist, a Brief Resilient Coping Scale, and suicide ideation/attempt across different time points (in the past week, month, year, and lifetime). Linear regression analyses were conducted to evaluate the extent to which the demographics, perceived health conditions and resilience correlated with overall suicide risk.
Results
A total of 2098 participants (50.6% females) were enrolled. The average resilience score in the general population was 16.75, with higher resilience score for those married and in a white-collar employment. Resilience showed positive correlations with increasing self-efficacy, self-rated mental health, and self-rated physical health, while displaying negative associations with psychological distress and overall suicidality. Linear regression analysis identified age, gender, employment status, marital status, self-rated physical health, self-efficacy, and resilience as predictors of overall suicide risk, explaining 16.8% of the variance.
Conclusions
Self-rated physical health, self-efficacy, and resilience were all associated with overall suicide risk in this study. Resilience also showed a statistically significant but modest independent effect in the multivariable model, suggesting that it contributes to suicide risk alongside these related psychological constructs. These findings indicate that perceptions of physical health and self-efficacy may be important targets for suicide prevention efforts, while strategies to strengthen resilient coping could also play a supportive role.
Clinical trial number
Not applicable.
Keywords: Resilience, Health perceptions, Mental distress, Suicidality, Population-based survey
Background
Suicide is a global public health issue. According to WHO data, over 700,000 people die by suicide annually [1], with prior suicidal ideation and suicide attempts increasing the risk of suicide [2]. Suicide risk affects individuals across all stages of life, believed to be as a result of a combination of sociocultural, biological, emotional, and psychological risk factors [3]. Suicide often occurs impulsively during crisis moments when the ability to cope with life stresses collapses, such as economic pressure, trauma, grief, chronic pain, and illness. Resilience as an individual’s intrinsic capacity to cope with such adversities has gained prominence in recent years. However, the relationship between resilience, psychological distress and suicidality is not well-understood among the general population. A better understanding of the relationship may inform targeted public health initiates and programs to enhance public mental health.
Resilience, as supported by various studies [4, 5], plays a crucial role in promoting both psychological and physical well-being. Resilience has been studied in specific groups such as adolescents [6], older adults, individuals with a history of suicide attempts, and those with chronic illnesses [7] and has been shown to be associated with well-being and positive health outcomes [8]. The concept of resilience was initially introduced in 1970 as a state or trait psychological construct [9]. Dyer and McGuinness (1996) later defined resilience as a dynamic process of recovering from adversity and returning to normal life [10]. Kalisch et al. (2015) further elaborated that resilience can also be viewed as an outcome [11]. When individuals struggle to cope with stress or manage crises, it can lead to the accumulation of problems, resulting in psychological distress and potentially increasing the risk of suicide [12]. Whereas, resilience enable individuals to restored or maintained their psychological or physical health even when exposed to significant stress or adversity [13].
Resilient coping is important in healthcare settings, where individuals with chronic and acute illnesses causing disability or impairment must rely on specific coping strategies to effectively manage and adapt to these significant challenges [7, 14]. In nursing, resilience is nurtured throughout the illness journey, with coping strategies helping individuals manage suffering and achieve calmness and recovery, promoting recovery, recalibration, and readjustment. Coping strategies are categorized into protective, compensatory, and challenge-related concepts, tailored to the trajectory of resilience development [15]. Protective strategies help individuals safeguard their assets and resources, especially in the early stages of illness. Compensatory strategies support coping mechanisms to manage current circumstances, while challenge-related strategies empower individuals to overcome physical and psychological obstacles, achieving tranquility and resilience.
Resilience development through effective coping strategies can be pivotal in preventing suicidal behavior, as low resilience may precede suicide attempts [13]. In a study by Hendricks et al. (2023), despair, depression, and existential reasons collectively accounted for 68% of the variance in resilience [12]. Psychological distress, mental disorder, including substance abuse can significantly increase the risk of suicide. The association between resilient coping strategies and psychological distress may indicate the significant potential of resilience to mitigate of psychological distress on suicide. Further, in understanding resilient coping in the context of psychological distress, there may be evidence to support the notion of the need to strengthen individual resilience for suicide prevention strategies.
The utility of resilience framework in suicide prevention is still at its infancy. We believe through better understanding of the relationship between resilient coping, psychological distress, and suicide has the potential to inform resilience-based suicide prevention and promotive initiatives in the general population. Therefore, in this study we hypothesized that a higher resilience level correlates with reduced psychological distress and suicidal ideation or other risks among the general population.
Methods
Study aims and designs
Using a representative sample, the study targeted at the general public in Taiwan and aimed to (1) explore the correlation between demographic characteristics and resilience levels; (2) investigate the associations between resilience and suicide risks; and (3) analyze the relationship between psychological distress, suicide risks, and resilience via inferential statistics.
In 2017, a nationwide survey collected information regarding mental health and suicide risks from a representative sample in Taiwan. The researchers performed secondary data analysis of the survey dataset and participated in the survey design process.
Sample and procedures
Participants were recruited through a telephone survey, which included individuals aged 15 and above participating in the mental health and suicide prevention survey. We employed stratified proportional randomization with the Computer Assisted Telephone Interview (CATI) System to sample based on population size, gender, and age distribution in various areas of Taiwan [16]. Participants under 18 obtained consents from their adult family members before the interview. Out of 4066 randomly selected landline numbers, 2098 respondents (51.6% response rate) agreed to participate anonymously, completing the interview with a sampling error of ± 2.10% (95% confidence interval).
Ethical considerations
The study received ethics approval from the Ethics Committee of National Taiwan University Hospital (Approval Number: 202103109 W).
Measurements
The brief resilient coping scale (BRCS)
Resilience was measured using the BRCS, which is a 4-item measure to capture tendencies to cope with stress. The four items contained the following questions: (1) I look for creative ways to alter difficult situations; (2) Regardless of what happens to me, I believe I can control my reaction to it; (3) I believe that I can grow in positive ways by dealing with difficult situations; (4) I actively look for ways to replace the losses I encounter in life. Each item was rated on a 5-point Likert scale (1–5), with a higher score representing higher levels of resilient coping levels. The total score of this scale ranges from 4 to 20 points [17]. The tertile of the scores was defined in the analysis and interpreted as follows, low (4–15), medium (16–17), high (18–20). It was also validated as a tool with good constructive validity. In this study, the internal consistency of Cronbach’s α values was 0.67.
Suicide risk assessment
Suicide risk level was assessed by determining whether respondents had experienced suicidal ideation at these various time points and whether they had engaged in suicide attempts, noting the timing of these attempts, including if they had ever occurred in their lifetime [16].The evaluation of suicide risk level encompassed three primary categories of variables: suicidal ideation (within the past week, month, year, and over a lifetime), suicide attempts (within the past month, year, and over a lifetime), and future suicide intent. Responses to these items were recorded as either yes (1) or no (0).
The 9-item concise mental health checklist (CMHC-9)
The CMHC-9 was to assess the overall suicide risk, which measures mental distress and suicidality across past, present, and future. It consists of nine items: The items to measure a person’s level of mental distress or psychopathological symptoms in the past seven days (e.g., insomnia, anxiety, hostility, depression, and inferiority) and to assess suicide risks (e.g., lifetime suicide attempt, future suicide intent, alcohol or drug abuse history, and lack of support as defined by no one trustworthy to talk). The response for each question was scored as 0 (no) or 1 (yes or present of symptoms). The total score ranges from 0 to 9 and was interpreted as follows: low suicide risk (total score < 4) or high suicide risk (total score ≧ 4) [18]. The Cronbach’s alpha of the CMHC-9 in this study was 0.70.
The 5-item brief symptom rating scale (BSRS-5)
BSRS-5 to measure the level of mental distress in the past week of the respondents. BSRS-5 is a 5-item Likert scale (scores of 0 to 4) that contained the following questions : (1) having trouble falling asleep (insomnia); (2) feeling tense or keyed up (anxiety); (3) feeling easily annoyed or irritated (hostility); (4) feeling low in mood (depression); and (5) feeling inferior to others (inferiority). The total score of the BSRS-5 measurement tool ranged from 0 to 20 points and was interpreted as follows: normal (total score < 6) or mental distress (total score ≧ 6) [16]. In this study, the internal consistency of Cronbach’s α values was 0.80.
Self-rated health measures
One item was used to assess health-related self-efficacy by the inquiry of, “How much confidence, from a scale of 0 to 100, do you think you have to control over your own health conditions?” The higher the score, the better the level of self-efficacy. The self-efficacy average score was 78, with 78 as the cut point, above as good, and below as poor. Moreover, two items assessed about self-rated health conditions in both physical and mental aspects, with the ratings from 0 (very poor) to 4 (very good) [16].
Demographic characteristics
The participants’ background information included gender, age, employment status, education, and marital status. These are all categorical variables shown in the table results except for age as a continuous variable.
Data analysis
Firstly, we calculated descriptive statistics for all study variables, providing means and standard deviations for continuous variables, and percentages for categorical variables. This involved an overall analysis and a specific examination related to suicide risk. Recognizing the significant skewness in the resilience score, we applied a log transformation for subsequent statistical analysis. To delineate distinct resilience levels, we established tertiles on the resilience scale, categorizing scores as follows: 4–15 (low), 16–17 (medium), and 18–20 (high). Furthermore, inferential statistics were conducted using chi-square tests, with resilience levels as the outcome variables. Demographic factors, alcohol or drug abuse, the absence of a trustworthy person to talk to, mental distress, self-rated health, and suicidality variables were treated as independent variables. Then, Spearman’s correlations were utilized to explore associations between resilience and marital status (marital status, single, divorced, widowed, and separated), employment status (white-collar, blue-collar, student, unemployed), mental distress, and suicidality. Additionally, a linear regression analyses was performed to understand the explanatory power of each variable (age, gender, marital status, employment status, self-rated health status, distress, and resilience) on the overall suicide risk measured by the CMHC-9. All analyses were carried out using SPSS v25, with statistical significance defined as p < 0.05.
Results
Resilience and participants characteristics
In total, there were 2098 participants in this study, with females comprising 50.6% of the sample. Nearly 70% of the participants fell within the 25–64 age group. Around 30% were unemployed, and 62.7% were married at the time of the interview. Examining the association of resilience with demographic variables, gender, age, and education showed no significant differences in resilience. However, there were notable associations between employment status and marriage with resilience. Notably, higher resilience was observed among employed and married participants. Specifically, a higher percentage of white-collar workers (34.7%) belonged to the high-resilient group compared to blue-collar (27.7%), student (9.9%), or unemployed (27.7%) participants. Additionally, a larger proportion of married respondents fell into the medium-to-high resilient groups (average of 64.1%), while only 32.2% and 3.7% of single and other marital statuses (divorced, widowed, or separated) were in the high-resilient groups, respectively (see Table 1).
Table 1.
The correlation between the three-level resilience and demographic variables
| Variables |
n/% (n = 2098) |
BRCS Mean(SD) |
Resilience tertile | χ2 | |||
|---|---|---|---|---|---|---|---|
| Low(4–15) (n = 492) |
Medium(16–17) (n = 790) |
High(18–20) (n = 816) |
|||||
| Gender | 2.32 | ||||||
| Male | 1036(49.4) | 16.9(± 2.3) | 229(46.6%) | 391(49.5%) | 416(51.0%) | ||
| Female | 1062(50.6) | 16.6(± 2.4) | 263(53.4%) | 399(50.5%) | 400(49.0%) | ||
| Age | 8.05 | ||||||
| 15–24 | 309(14.7) | 16.7(± 2.3) | 77(15.7%) | 113(14.3%) | 119(14.6%) | ||
| 25–44 | 742(35.4) | 16.9(± 2.3) | 159(32.4%) | 288(36.5%) | 295(36.1%) | ||
| 45–64 | 720(34.3) | 16.8(± 2.4) | 163(33.0%) | 267(33.8%) | 290(35.6%) | ||
| ≧ 65 | 327(15.6) | 16.3(± 2.3) | 93(18.9%) | 122(15.4%) | 112(13.7%) | ||
| Education | 7.79 | ||||||
| Below senior high | 1020(48.6) | 16.6(± 2.5) | 244(49.7%) | 406(51.3%) | 370(45.3%) | ||
| Associate/bachelor’s | 937(44.7) | 17.0(± 2.3) | 210(42.6%) | 334(42.4%) | 393(48.2%) | ||
| Graduate degree | 141(6.7) | 16.6(± 2.6) | 38(7.7%) | 50(6.3%) | 53(6.5%) | ||
| Job | 14.32* | ||||||
| White collar | 667(31.8) | 16.9(± 2.4) | 142(28.9%) | 242(30.6%) | 283(34.7%) | ||
| Blue collar | 565(26.9) | 16.9(± 2.2) | 116(23.6%) | 223(28.2%) | 226(27.7%) | ||
| Student | 226(10.8) | 16.5(± 2.4) | 64(13.0%) | 81(10.2%) | 81(9.9%) | ||
| Unemployed | 640(30.5) | 16.4(± 2.6) | 170(34.5%) | 244(31.0%) | 226(27.7%) | ||
| Marriage | 22.28** | ||||||
| Married | 1314(62.7) | 16.9(± 2.4) | 267(54.2%) | 524(66.3%) | 523(64.1%) | ||
| Single | 711(33.9) | 16.6(± 2.4) | 201(40.9%) | 247(31.3%) | 263(32.2%) | ||
|
Divorced, windowed, and separation |
73(3.5) | 16.2(± 3.0) | 24(4.9%) | 19(2.4%) | 30(3.7%) | ||
*P < 0.05, **P < 0.01; SD: standard deviation, BRCS: Brief Resilient Coping Scale
Relationship of resilience with perceived health and psychological distress
The highest resilient group of participants had a mean self-efficacy score of 81.2%, compared to 72.6% for the lowest group. Among all three categorical variables, respondents with the lowest resilient levels had lower scores in each variable, ranging from 45.2% to 73.8%. Moreover, participants with high psychological distress (> 6) showed poor resilience, and vice versa. All five psychological distress variables were negatively correlated with resilience. Among the three levels of resilience, respondents with the lowest resilient levels had higher mental distress in each variable, especially in hostility and insomnia (Table 2).
Table 2.
The correlation between the three-level resilience with perceived health and mental distress
| Variable, n(%)/ Mean (SD) |
Resilience | |||
|---|---|---|---|---|
| Low (4–15) (n = 492) |
Medium (16–17) (n = 790) |
High (18–20) (n = 816) |
F / χ2 | |
| Self-efficiency score | 72.6 (± 14.7) | 78.2 (± 12.6) | 81.2 (± 12.2) | 65.53** |
| Self-efficiency (n = 2066) | 123.77** | |||
| Poor (below mean) | 258(54.8%) | 269(34.3%) | 196(24.1%) | |
| Good (above mean) | 213(45.2%) | 512(65.6%) | 618(75.9%) | |
| SR mental health | 89.03** | |||
| Poor | 36(7.3%) | 11(1.4%) | 8(1.0%) | |
| Medium | 94(18.9%) | 76(9.6%) | 70(8.6%) | |
| Good | 362(73.8%) | 703(89.0%) | 738(90.4%) | |
| SR physical health | 50.17** | |||
| Poor | 68(13.8%) | 51(6.4%) | 40(4.9%) | |
| Medium | 101(20.4%) | 125(15.9%) | 119(14.6%) | |
| Good | 323(65.8%) | 614(77.6%) | 657(80.5%) | |
| Mental distress (BSRS-5) | 63.78** | |||
| Low (< 6) | 422(85.7%) | 757(95.8%) | 782(96.0%) | |
| High (≥ 6) | 70(14.3%) | 33(4.2%) | 34(4.0%) | |
| BSRS-5 total | 2.24(± 3.38) | 1.14(± 2.01) | 1.08(± 1.89) | 43.52** |
| BSRS_Item 1. Insomnia | 7.62* | |||
| No | 350(71.1%) | 608(77.0%) | 632(77.5%) | |
| Yes | 142(28.9%) | 182(23.0%) | 184(22.5%) | |
| BSRS_Item 2. Anxiety | 35.45** | |||
| No | 366(74.4%) | 673(85.2%) | 705(86.4%) | |
| Yes | 126(25.6%) | 117(14.8%) | 111(13.6%) | |
| BSRS_Item 3. Hostility | 34.73** | |||
| No | 336(68.3%) | 638(80.8%) | 661(81.0%) | |
| Yes | 156(31.7%) | 152(19.2%) | 155(19.0%) | |
| BSRS_Item 4. Depression | 46.35** | |||
| No | 362(73.6%) | 688(87.1%) | 702(86.0%) | |
| Yes | 130(26.4%) | 102(12.9%) | 114(14.0%) | |
| BSRS_Item 5. Inferiority | 44.43** | |||
| No | 361(73.4%) | 673(85.2%) | 710(87.0%) | |
| Yes | 131(26.6%) | 117(14.8%) | 106(13.0%) | |
*P < 0.05, **P < 0.01, SR: Self-related, SD: standard deviation, BSRS-5: The 5-item Brief Symptom Rating Scale
Resilience and suicidality
The study found that suicidal ideation was negatively correlated with resilience, whether for a week, month, year, or lifetime. This indicates that the resilience of those who had suicidal ideation was consistently poor and not influenced by time. Only past-year suicidal attempts and resilience showed no significant correlation. Although suicide attempts are rare, they are crucial in the general population (Table 3).
Table 3.
The correlation between resilience and suicidality
| Variables | Resilience^ | |||
|---|---|---|---|---|
| Low (n = 492) |
Medium (n = 790) |
High (n = 816) |
F / χ2 | |
| SI_Lifetime | 41.29** | |||
| No | 396(80.5%) | 707(89.5%) | 751(92%) | |
| Yes | 96(19.5%) | 83(10.5%) | 65(8.0%) | |
| SI_Year# | 48.07** | |||
| No | 469(95.3%) | 787(99.5%) | 812(99.6%) | |
| Yes | 23(4.7%) | 4(0.5%) | 3(0.4%) | |
| SI_Month# | 11.25** | |||
| No | 483(98.2%) | 787(99.6%) | 813(99.6%) | |
| Yes | 9(1.8%) | 2(0.4%) | 3(0.4%) | |
| SI_Week# | 25.26** | |||
| No | 476(96.7%) | 786(99.5%) | 812(99.5%) | |
| Yes | 16(3.3%) | 4(0.5%) | 4(0.5%) | |
| SA_Lifetime | 0.64 | |||
| No | 487(97.2%) | 773(97.8%) | 797(97.7%) | |
| Yes | 14(2.8%) | 17(2.2%) | 19(2.3%) | |
| SA_Year# | 9.81** | |||
| No | 489(99.4%) | 790(100%) | 816(100%) | |
| Yes | 3(0.6%) | 0(0.0%) | 0(0.0%) | |
| Future_Suicide intention | 6.29 | |||
| No | 477(97.0%) | 781(98.9%) | 802(98.3%) | |
| Yes | 15(3.0%) | 9(1.1%) | 14(1.7%) | |
| No one trustworthy to talk | 16.25** | |||
| No | 421(85.6%) | 726(91.9%) | 747(91.5%) | |
| Yes | 71(14.4%) | 64(8.1%) | 69(8.5%) | |
| Alcohol or drug abuse | 6.40* | |||
| No | 476(96.7%) | 771(97.6%) | 806(98.8%) | |
| Yes | 16(23.5%) | 19(2.4%) | 10(1.2%) | |
| Suicide risk (CMHC-9) | 61.13** | |||
| Low (< 4) | 397(80.7%) | 730(92.4%) | 760(93.1%) | |
| High (≥ 4) | 95(19.3%) | 60(7.6%) | 56(6.9%) | |
| CMHC-9 (mean ± SD) | 1.63 (± 1.95) | 0.99 (± 1.41) | 0.96 (± 1.35) | 34.44** |
*P < 0.05, **P < 0.01, SA: Suicide attempt, SI: Suicide ideation, CMHC-9: The 9-item Concise Mental Health Checklist. SD: standard deviation
^ The higher the score of resilience scale, the better the level of resilient coping
# The Fisher's Exact Test was applied when sample sizes are smaller than 5
Correlation of resilience with perceived health, mental distress, and suicidality
In Spearman’s correlation analysis, all variables exhibited a low correlation with resilience. For instance, resilience demonstrated positive correlations with self-related health variables (r = 0.236, r = 0.270, and r = 0.193 for self-efficacy, self-rated mental health, and self-rated physical health, respectively). Conversely, resilience showed negative correlations with psychological distress (BSRS-5) and overall suicide risk (CMHC-9) (r = -0.145 and r = -0.147, respectively). Resilience also displayed negative correlations with suicidal ideation, specifically for lifetime and last year (r = -0.130 and r = -0.125, respectively) (refer to Table 4). These findings indicate that higher adaptability aligns with stronger self-related health variables. Moreover, psychological distress, overall suicide risk, and suicidal ideation were inversely associated with resilience, suggesting that factors such as poor social support and substance addiction may contribute to lower resilience levels.
Table 4.
Spearman’s correlation matrix of the associations between resilience and sociodemographics, perceived health, mental distress, and suicidality
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.Resilience | 1 | ||||||||||
| 2.Job | 0.068** | 1 | - | ||||||||
| 3.Married | 0.064** | − 0.048* | 1 | ||||||||
| 4.Self-efficiency | 0.253** | 0.022 | -0.022 | 1 | |||||||
| 5.SR-MH | 0.270** | 0.021 | 0.030 | 0.461** | 1 | ||||||
| 6.SR-PH | 0.193** | 0.094** | -0.022 | 0.506** | 0.566** | 1 | |||||
| 7.SI-year | − 0.125** | − 0.076** | − 0.064** | − 0.100** | − 0.214** | − 0.159** | 1 | ||||
| 8.SI-month | − 0.061** | − 0.074** | -0.005 | − 0.062** | − 0.167** | − 0.114** | 0.678** | 1 | |||
| 9.SA-Year | − 0.056* | -0.019 | − 0.072** | − 0.047* | − 0.060** | -0.041 | 0.311** | -0.003 | 1 | ||
| 10.SI-week | − 0.093** | − 0.082** | -0.024 | − 0.092** | − 0.177** | − 0.118** | 0.388** | 0.494** | -0.004 | 1 | |
| 11.CMHC-9 | − 0.147** | -0.019 | − 0.127** | − 0.294** | − 0.379** | − 0.320** | 0.293** | 0.215** | 0.105** | 0.290** | 1 |
*P < 0.05, **P < 0.01. SR_MH: self-rated mental health, SR_PH: self-rated physical health, SI: suicide ideation, SA: suicide attempt, CMHC-9: The 9-item Concise Mental Health Checklist
This study demonstrated that higher interpersonal resilience is linked to better perceived confidence or health status. In other words, lower levels of resilience are often correlated with adverse psychological distress, suicidal ideation/attempts, or a lack of social support. Table 5 presents the linear regression models predicting the overall suicide risk among the general public. In Model 1, which included only sociodemographic variables, the model explained 1.9% of the variance in overall suicide risk (R² = 0.019, F = 10.017, p < 0.01). Gender (β = -0.236, p = 0.001) and marital status (β = -0.434, p < 0.001) were significant predictors, whereas age (β = 0.070, p = 0.111) and job status (β = 0.068, p = 0.334) were not significant. In Model 2, the addition of self-rated health variables and self-efficacy substantially improved the model, explaining 16.6% of the variance (R² = 0.166, F = 69.178, p < 0.01). Self-rated physical health (β = -0.341, p < 0.001) and self-efficacy (β = -0.029, p < 0.001) were significant predictors, while the effects of gender and marital status remained significant. In Model 3, resilience (BRCS score) was further added and the explained variance increased slightly to 16.8% (R² = 0.168, F = 60.235, p < 0.01). Resilience showed a small but significant association with overall suicide risk (β = -0.032, 95% CI [-0.059, -0.006], p = 0.018), whereas self-rated physical health and self-efficacy remained significant predictors.
Table 5.
Linear regression models predicting the overall suicide risk among the general public
| Mode 1 | Mode 2 | Mode 3 | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Β | 95%CI Lower Bound |
95%CI Upper Bound |
p-Value | Β | 95%CI Lower Bound |
95%CI Upper Bound |
p-Value | β | 95%CI Lower Bound |
95%CI Upper Bound |
p-Value | |
| Intercept | 1.299 | 1.076 | 1.522 | < 0.001 | 5.144 | 4.696 | 5.592 | < 0.001 | 5.547 | 4.990 | 6.105 | < 0.001 |
| Sociodemographics | ||||||||||||
| Age | 0.070 | -0.016 | 0.157 | 0.111 | -0.059 | -0.141 | 0.022 | 0.152 | -0.063 | -0.145 | 0.018 | 0.125 |
| Gender | -0.236 | -0.370 | -0.102 | 0.001 | -0.214 | -0.338 | -0.090 | 0.001 | -0.209 | -0.333 | -0.085 | 0.001 |
| Job status | 0.068 | -0.070 | 0.206 | 0.334 | 0.081 | -0.047 | 0.208 | 0.215 | 0.092 | -0.035 | 0.220 | 0.156 |
| Marital status | -0.434 | -0.600 | -0.269 | < 0.001 | -0.355 | -0.508 | -0.202 | < 0.001 | -0.339 | -0.492 | -0.186 | < 0.001 |
| Self-rated health variables | ||||||||||||
| SR Physical Health | -0.341 | -0.423 | -0.259 | < 0.001 | -0.339 | -0.492 | -0.251 | < 0.001 | ||||
| Self-efficacy | -0.029 | -0.034 | -0.023 | < 0.001 | -0.027 | -0.033 | -0.022 | < 0.001 | ||||
| Resilience (BRCS score) | -0.032 | -0.059 | -0.006 | 0.0182 | ||||||||
| R2 | 0.019 | 0.166 | 0.168 | |||||||||
| ΔR2 | 0.017 | 0.163 | 0.165 | |||||||||
| F | 10.017** | 69.178** | 60.235** | |||||||||
**p < 0.01, SI: suicide ideation, SR: self-rated, overall suicide risk (CMHC-9 score): The 9-item Concise Mental Health Checklist, BRCS: Brief Resilient Coping Scale
Discussion
The study’s findings revealed an average resilience score of 16.8 in the general population. Resilience measured using BRCS showed positive correlations with self-efficacy(r = 0.236), self-rated mental health(r = 0.270), and self-rated physical health(r = 0.193), while displaying negative correlations with BSRS-5 (r=-0.145), CMHC-9 (r=-0.147), and lifetime/past-year suicidal ideation (r=-0.130 and r=-0.125, respectively). Moreover, the linear regression analysis showed that self-rated physical health, self-efficacy, and resilience together explained 16.8% of the variance in overall suicide risk. Resilience showed a small but statistically significant association with overall suicide risk (β = -0.032, 95% CI [-0.059, -0.006], p = 0.018), while self-rated physical health and self-efficacy remained significant predictors. These findings suggest that self-rated physical health and self-efficacy account for most of the explained variance in suicide risk, with resilience making only a modest additional contribution in this multivariate context. Importantly, resilience was independently associated with overall suicide risk in spite of controlling self-efficacy, indicates resilient coping is an important psychological capacity in addition to health-related self-efficacy. While self-efficacy may play an important role in individual agency to seek help and taking control of their health; coping resilience may be important for adapting to changes and life events. Individuals with lower levels of resilient coping may have a reduced capacity to manage stress and adversity, which could increase vulnerability to depression, anxiety, sleep problems, and suicidal ideation, as suggested in previous resilience–suicidality research. This pattern is evident across diverse populations and contexts. For instance, Llistosella et al. (2022) [19] conducted their study during the COVID-19 pandemic in Spain and found that those facing mental health challenges, both before and after the pandemic, exhibited diminished resilience and a significantly increased risk of depressive episodes and suicidal behaviors compared to healthy individuals. Notably, the study did not focus on studying the direct link between COVID-19 exposure and psychological distress or suicide risk. However, evidence showed that low resilience was associated with suicide risk in populations of older adults and individuals with mental or chronic illnesses [13, 20]. Physical and cognitive decline in the aged could lead to frustrations in daily life, resulting in a reluctance to engage in social interactions and even social isolation. This creates a vicious cycle. Over time, isolation may lead to psychological distress, such as anxiety, depression, and feelings of inferiority, with which suicidal ideation or attempts occur.
Older adults’ low resilience is often linked to an increased risk of suicidal ideation and behavior [13]. The “Protection” concept in “The Resilience Framework for Nursing and Healthcare” focuses on building community networks and organizing activities that encourage adults to interact with other adult residents [15]. This strategy aims to reduce social isolation and emotional distress, enhance community integration, and ultimately increase individual resilience while decreasing the risk of suicidal ideation. Thus, resilience plays a crucial role in reducing suicidal ideation and behavior, through strengthening coping mechanisms and protective factors against suicide risk [12, 21].
Our study found an average resilience score of 16.75 across a broad age range in the public, while middle-aged patients with arthritis exhibited lower resilience levels of 14.81 [17]. The higher resilient coping abilities observed in our study were partially attributable to the supportive influence of both community and marital contexts, which appear to foster self-perceived resilience closely associated with self-rated mental health, self-rated physical health, and self-efficacy [22]. Individuals with higher resilience approach physical health challenges with positivity and optimism, utilizing problem-solving skills to overcome obstacles [8]. Conversely, poor mental and physical health, including mental illness and chronic diseases, often coincide with lower levels of health control and self-efficacy, negatively impacting personal resilience [7, 23]. Social support is also crucial, as stronger support correlates with higher resilience scores [24]. This difference may arise from cultural variations, especially in Eastern cultures that prioritize collectivism and value family and social relationships, where social support and positive interpersonal interactions are crucial for strengthening resilience [25]. According to Durkheim’s theory of suicide social integration, marriage can lower the risk of suicide by fostering social support [26], as marital relationships promote altruism and social cohesion. However, this integration is disrupted by divorce and bereavement, leading to a decrease in resilience and an increased risk of suicide. Therefore, understanding individual needs and strengthening social connections are essential for effectively adapting to and overcoming stressors and challenges comprehensively.
In this study, we observed a negative association between psychological distress and resilience, particularly in emotional aspects such as depression, and low self-efficacy among the general population. Our finding of an association between psychological distress and resilience is consistent with previous studies [27]. Low resilience has been linked to psychological distress and mental illness, potentially creating a detrimental cycle of anxiety, worry, and insomnia in the face of stressors [27]. The absence of resilient traits, like positivity and optimism, can contribute to problems with anxiety and insomnia, establishing a vicious cycle of worsening mental symptoms. Severe psychological distress, often manifesting as depression, is a key factor in low resilience. Notably, age influences the stress response, with younger individuals experiencing more severe psychological distress compared to older adults, who demonstrate greater resilience [28]. Resilience emerges as a crucial protective factor against suicidal ideation and attempts. Studies, including one with US veterans, have consistently shown higher suicidal ideation and attempts when resilience is low [29]. Another examination across different age groups found low resilience associated with increased suicide risk, with significantly lower resilience scores in cases of suicide attempts [6, 8]. Therefore, enhancing resilience is crucial as low resilience, influenced by various factors (e.g., personal traits, chronic illness, family background, social support, interpersonal relationships), often leads to negative emotions and psychological distress, potentially increasing risk for suicidal ideation or attempts. To address this, a comprehensive approach is recommended, focusing on bolstering personal traits like self-efficacy, self-esteem, and positive attitudes, as well as fostering interpersonal relationships and active social participation. Given these findings, it is imperative to enhance personal resilience by strengthening internal capacities and external resources. Tailored interventions, adjusted for different ages and backgrounds, can mitigate psychological disturbances, and reduce the occurrence of suicidal behaviors.
High resilience is closely associated with enhanced abilities to manage illness, improved social skills, and greater social support. Our study establishes significant correlations between resilience and health-related measures, including self-efficacy, self-rated mental health, and self-rated physical health. Correlation analysis revealed that resilience was significantly associated with self-rated mental health, self-rated physical health, and self-efficacy. Specifically, higher levels of resilience were associated with better self-rated mental health, better self-rated physical health, and higher self-efficacy. This underscores the interconnected nature of these psychological and health-related variables. Notably, self-rated mental health conditions such as depression and anxiety substantially reduce quality of life, resulting in lower resilience levels—a finding consistent with previous research by Anyan and Hjemdal (2016) [27]. Research by Ordway et al. (2020) suggests that health issues and chronic pain also significantly impact resilience [30]. Moreover, resilience appears to improve cognitive abilities related to chronic diseases such as systemic lupus erythematosus, diabetes mellitus, and rheumatoid arthritis, aiding individuals in managing chronic pain and developing coping strategies for living with these conditions [7]. So, implementing a resilience challenge concept revealed that individuals facing chronic illnesses and persistent pain must learn coping mechanisms and address associated discomfort. Providing resources and support networks can help mitigate the impact of disease-related challenges. Furthermore, Anyan and Hjemdal (2016) emphasize that resilience plays a crucial role in moderating the relationship between stress and depression, buffering against the onset of depressive symptoms during stressful periods, thereby providing a protective function [27].
Limitations
This study has several limitations. First, the cross-sectional design precludes causal inference; therefore, results should be interpreted as correlational associations only. Notably, the small but statistically significant association between resilience and overall suicide risk may reflect overlapping constructs within the multivariate model. Future research should employ longitudinal designs to explore causal relationships among resilience, self-rated health variables, and overall suicide risk in the general population. Second, landline telephone surveys may introduce selection bias by excluding homeless individuals, mobile-only users, and households without landline access. Future studies may incorporate mobile phone surveys or home visits to enhance population representativeness. Third, the absence of data on participants’ psychiatric history may introduce residual confounding and limit interpretation of findings, as underlying mental health conditions may not be fully captured. Lastly, the exclusion of cases with psychological problems or psychiatric disorders that may have not responded to the survey limited our ability to explore resilience and suicide risk in high-risk populations. It remains unclear how their resilience compares to that of the general population.
Implication
The study provides important insights into the correlation between self-rated health, psychological distress, suicide risk, and resilience among the general population. It is important for healthcare professionals to be sensitive to differentiate lower resilience and better coping potentials via personal health perceptions and suicide risk detection, enabling early identification and referral for further assessment and intervention to prevent mental health crisis.
Conclusions
This study suggests that self-rated physical health, self-efficacy, and resilience were associated with an overall suicide risk in a general population in Taiwan, although resilience makes only a modest but statistically significant independent contribution after controlling for the other variables. Higher levels of psychological distress and suicidal ideation or attempts were also linked to reduced coping capacity among participants. Together, these findings point to substantial conceptual overlap among the psychological constructs examined and indicate that resilience may operate partly through mental health pathways rather than as a completely distinct factor on suicide risk. Consequently, suicide prevention efforts may benefit from integrated approaches that enhance mental health literacy, strengthen self-efficacy, and foster resilient coping, while clinical assessments systematically consider subjective health perceptions to better identify vulnerable populations.
Acknowledgements
The authors would like to thank all the staff at the Taiwanese Society of Suicidology for their administrative assistance throughout the study.
Author contributions
Conception or design of the work: CYW; analysis or interpretation of data: CYW, WCH, CSM, and VI; drafting the paper or critically revising the contents: WCH, CYW, and VI; final approval of the draft for submission: CYW, WCH, CSM, and VI. All authors have agreed to the published version of the manuscript.
Funding
The authors received no specific funding for this work.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This study was conducted in strict accordance with the Declaration of Helsinki and approved by the institutional review board of the National Taiwan University Hospital (Approval Number: 202103109 W). All participants were informed about the aims and procedures of the study. This study included anonymous representative telephone interviews; therefore, the survey was initiated after verbal consent. Additionally, verbal consent from a legal guardian was acquired for participants under the age of 18.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.