Skip to main content
NCBI home page
Elsevier - PMC COVID-19 Collection logoLink to Elsevier - PMC COVID-19 Collection
. 2023 Jan 23;252:309–316. doi: 10.1016/j.schres.2023.01.027

Psychotic-like experiences during COVID-19 lockdown among adolescents: Prevalence, risk and protective factors

Dongfang Wang a,b,1, Liang Zhou a,1, Chunping Chen c, Meng Sun a,⁎
PMCID: PMC9868397  PMID: 36706476

Abstract

Objective

Although plenty of evidence has shown the huge negative impact of COVID-19 on individuals' mental health conditions, little is known about its impact on the psychotic-like experiences (PLEs) in the general population. We aim to explore the prevalence of PLEs and relevant influential factors among adolescents during COVID-19 lockdown.

Methods

A total of 3234 students completed one online survey between April to May 2020. PLEs were assessed using the 15-item Positive Subscale of the Community Assessment of Psychic Experiences (CAPE-P15). Resilience, social support, childhood trauma, and a series of socio-demographic factors were also evaluated.

Results

In this sample, 51.4 % adolescents reported having at least one PLE, while 11.6 % experienced PLEs frequently during COVID-19 lockdown. Senior high school students showed more frequent PLEs than college students (p < 0.001). Female gender (OR = 1.77), history of mental disorders (OR = 3.07) or chronic physical illness (OR = 2.04), having relatives or friends being infected with COVID-19 (OR = 3.12), longer daily exposure to media coverage of the COVID-19 (OR = 1.60), and more childhood trauma (OR = 1.54–3.49) were correlated with more frequent PLEs, while higher resilience (OR = 0.35–0.54) and more perceived social support (OR = 0.63–0.72) were associated with decreased odds for frequent PLEs. Additionally, there were several differences among the influential factors between senior high school and college students.

Conclusions

PLEs were relatively common among Chinese adolescents, with higher prevalence among senior high school students during COVID-19 lockdown. Adolescents with specific characteristics should receive more attention in the development of intervention measures in mental health during pandemic lockdown.

Keywords: Psychotic-like experiences, Adolescents, COVID-19, Lockdown

1. Introduction

Coronavirus disease 2019 (COVID-19), as a public health emergency of international concern (WHO, 2020), has led to adverse mental health outcomes among the public (Vindegaard and Benros, 2020). There is a growing evidence of a pronounced increase in mental health problems (e.g., anxiety, depression, insomnia, and posttraumatic stress symptoms) among front-line medical staff (Lai et al., 2020; Liu et al., 2020a), university students (Kaparounaki et al., 2020; Odriozola-Gonzalez et al., 2020), and the general population (Casagrande et al., 2020; Zhao et al., 2021), during the pandemic.

Psychosis is also one mental health condition that requires specific attention during the pandemic. The relationship between influenza infection and psychosis, known as acute “psychosis of influenza”, has been documented since the 18th century (Kepinska et al., 2020). A rapid review suggested that the incidence of psychosis may increase during pandemic-like experiences, (e.g., severe acute respiratory syndrome (SARS), or Middle East respiratory syndrome (MERS)), with an incidence of 0.9 % to 4 % in people infected with a virus, and a 25 % increase in people not infected with a virus (Brown et al., 2020). Severance et al. showed that HKU1 and NL63 coronavirus were associated with a 32 % and 142 % increase in the likelihood of psychosis (Severance et al., 2011). During the COVID-19 lockdown, psychiatric disorders increased by 6.8 %, with schizophrenia and acute transitional psychosis being particularly prominent (Jagadheesan et al., 2021). Recent two case reports also revealed that people with no history of psychiatric condition developed psychotic symptoms during COVID-19 lockdown (Belvederi Murri et al., 2021; Oloniniyi et al., 2021). However, most studies focus on the pandemic's impact on psychiatric patients or the symptoms of psychosis in people infected with the virus (Brown et al., 2020), related research among the general population is sparse.

Psychotic-like experiences (PLEs) refer to experiences that resemble the positive symptoms of psychosis lying on a spectra of severity in the general population (Kelleher and Cannon, 2011), which predict the later onset of full-blown psychiatric disorders (Healy et al., 2019). PLEs are not uncommon, especially in adolescents (Kelleher et al., 2012). However, only those occurring frequently or persistantly have a strong relationship with clinical transformation (Kaymaz et al., 2012), which should be placed emphasis on. Up to date, frequent PLEs have been reported to be associated with numerous psychosocial factors. For instance, left-behind experience, that is, having left behind in their hometown by one or both of their migrant worker parents for over six months before age 16 (UNICEF, 2018), is associated with an increased risk of PLEs (Sun et al., 2017a). A higher prevalence of PLEs has been found in adolescents living in urban areas compared to those in rural areas by Sun et al. (2015). Moreover, childhood trauma (Lu et al., 2020) has also exhibited to be a potential risk factor for PLEs, while resilience (Metel et al., 2020) appears to serve as a protective factor against PLEs.

Moreover, adolescence seems to be a demographic that has experienced a greater psychological impact from the COVID-19 (Wang et al., 2020). Since the outbreak of COVID-19, many countries, including China, have taken confinement measures, including contact restrictions, self-isolation, and closure of schools, colleges, or universities to deal with the spread of the pandemic (Bedford et al., 2020). Students have to stay at home to complete their course online. Among these adolescents, those senior high school students should receive more attention because of their considerable pressure of entering a higher school in China. In addition, the mental health of the college student population may also need more attention in the pandemic era (Wang et al., 2022), as the uncertainty of future career or academic opportunities due to the lockdown further increased their more psychological stress (Cao et al., 2020; Cornine, 2020). Although regular epidemic prevention and control period measures have been implemented in China, lockdown will still be enforced where there are COVID-19 confirmed cases, to stop the spread of the epidemic. Therefore, epidemiological studies on PLEs in adolescents during lockdown are necessary to better understand the impact of pandemic lockdown, as well as to inform effective intervention strategies targeting this vulnerable group.

Although the COVID-19 pandemic and subsequent quarantine have adverse impact on mental health among adolescents (Guessoum et al., 2020; Singh et al., 2020), limited research has examined the association between COVID-19 lockdown and PLEs. One recent study involving 1825 junior high school students have shown an elevated prevalence of PLEs after the COVID-19 outbreak (Wu et al., 2021). Another research with small sample size (N = 166) has also reported an increase in the prevalence of PLEs during COVID-19 lockdown (Simor et al., 2021). However, the prevalence of PLEs among senior high school and college students and their related influential factors during the lockdown remain unclear.

In China, COVID-19 broke out in December 2019, and quickly spread in the country. As of May 2020, the pandemic in China has been effectively under control. Since then, many middle schools and colleges/universities have started to re-open and allowed students to return to school in batches (Wang et al., 2021a). Accordingly, we conducted a cross-sectional survey on PLEs over the past three months in high school and college students from April 2020 to May 2020. We aim to explore the prevalence of PLEs among adolescents during COVID-19 lockdown, to identify the characteristics among those who were at highest risk of frequent PLEs across this period, as well as to explore the differences in prevalence and influencing factors of PLEs among senior middle school students and college students.

2. Materials and methods

2.1. Sample

A convenience sample of students was collected through online survey from 7 senior high school and 9 universities/colleges in four provinces (Guangdong, Henan, Hunan, Zhejiang) in China, during the COVID-19 lockdown from April 2020 to May 2020. The cumulative COVID-19 confirmed cases in these four provinces both ranges from 1000 to 9999, which were pandemic moderate-risk areas assessed by the World Health Organization in early 2020 (WHO, 2020).

Participants with response time less than five minutes and those with history of psychotic disorders were excluded. Considering that the COVID-19 infection may affect the nervous system (Heneka et al., 2020), those reported being infected with COVID-19 were also excluded. All students (or their guardians, if age of students <16 years old) signed the electronic informed consent before starting the online survey. This survey was entirely voluntary, and participants could quit at any time. This study was carried out in accordance with the latest version of the Declaration of Helsinki and approved by the Ethics Committees of the Affiliated Brain Hospital of Guangzhou Medical University.

2.2. Measures

2.2.1. Socio-demographic characteristics

Socio-demographic characteristics collected in this survey included sex, age, ethnicity, residence province, residence location, single child status, history of mental disorders, chronic physical illness, family history of mental disorders, parental marital status, left-behind experience, family income (RMB per month), whether relatives or friends being infected with COVID-19, and daily duration exposure to media coverage of the COVID-19.

2.2.2. PLEs

PLEs were assessed by the frequency subscale of the 15-item Positive Subscale of the Community Assessment of Psychic Experiences (CAPE-P15) (Capra et al., 2013). All 15 items were categorized into three factors: persecutory ideation (PI), bizarre experiences (BEs), and perceptual abnormalities (PAs). Each item was rated on a four-point Likert scale, from 1 (never), 2 (sometimes), 3 (often), to 4(nearly always). Participants were defined as having frequent PLEs if they selected “often” or “nearly always” on one or more items in this study (Sun et al., 2017a).

The Chinese version of the CAPE-P15 has showed satisfactory psychometric properties among senior high school students (Wang et al., 2021b) and college students (Sun et al., 2020). The CAPE-P15 also provides a valid and reliable measure of PLEs over past three months (Capra et al., 2017). In this study, 3-month version of CAPE-P15 was first used in Chinese population. The three-correlated factor model (PI, BEs, PAs) showed acceptable fit to this sample (χ2 = 382.39, df = 87, CFI = 0.94, TLI = 0.93, RMSEA = 0.03, SRMR = 0.04) (see Fig. 1 ), and the Cronbach's alpha was 0.90.

Fig. 1.

Fig. 1

Open in a new tab

Three-factor model of the 3-months version of CAPE-P15.

2.2.3. Resilience, social support, and childhood trauma

Resilience was measured through the Chinese version of the 10-item Connor-Davidson Resilience Scale (CD-RISC-10) (Wang et al., 2010). Response to each item ranges from 0 (never) to 4 (nearly always), with higher total scores indicating better resilience. The Cronbach's α was 0.86 in this sample. Social support was assessed through the Chinese version of the Multidimensional Scale of Perceived Social Support (MSPSS) (Wang et al., 2017). The MSPSS is a 12-item scale, with each item scored from 0 (very strongly disagree) to 7 (strongly agree). Higher total scores indicate greater level of perceived social support. This scale had a good internal consistency in this sample, with a Cronbach's α of 0.95. The Chinese version of 28-item Childhood Trauma Questionnaire -Short Form (CTQ-SF) was used to measure adolescent' subjective experiences of childhood trauma before their 16 years old (He et al., 2019). The CTQ-SF asks participants to report the level of agreement with 5 items on a 3-point scale (form 1- never or rarely to 5-always). Higher total scores indicate more childhood trauma experienced. In this study, the Cronbach's α was acceptable (α = 0.76). According to previous studies (Fan et al., 2015), resilience, social support, and childhood trauma were classified into three categories, with low and high categories defined by the 27th and 73rd percentile.

2.3. Statistical analysis

All analyses were conducted with SPSS 23.0. Descriptive statistics were calculated for socio-demographic characteristics. The percentage was used to reflect the prevalence of PLEs, and the χ2 test was used to compare the prevalence of frequent PLEs between senior high students and college students. An intercept model was established initially to evaluate the school/college-level heterogeneity of PLEs. The potential multicollinearity of all variables was also evaluated through the variance inflation factor (VIF) (O'Brien, 2007). The multivariate logistic regressions were further used to examine risk and protective factors for frequent PLEs among the whole sample, and then among senior high students and college students, respectively. Socio-demographic characteristics, resilience, social support, and childhood trauma were entered as independent variables, using forward entry method. The results were displayed with odds ratios (ORs) and 95 % confidence intervals (95 % CIs). A two-sided p < 0.05 was considered statistically significant.

3. Results

3.1. Characteristics of the sample

A total of 3423 student students participated in this online survey, with 161 refusing to participate. Twenty-three students were excluded due to excessive age (>25 years old) or low quality of the survey responses (the response time for survey <5 min). Besides, five participants who reported being infected with the COVID-19, were also excluded for the subsequent analysis, leaving 3234 valid responses.

The average age for the retained participants was 18.60 years (SD = 1.67). Among these students, 1.0 % (N = 32) reported a history of mental disorders (14 major depression disorders, 16 anxiety disorders, 1 obsessive-compulsive disorders, 5 bipolar disorders, and 6 others), while none of them were psychotic disorders. Other detailed socio-demographic information is presented in Table 1 .

Table 1.

Sample characteristics [N (%)].

Variables Characteristics Overall
N = 3234		 Senior high school
N = 805		 College
N = 2429
Sex Male 1256 (38.8) 381 (47.3) 875 (36.0)
Female 1978 (61.2) 424 (52.7) 1554 (64.0)
Age [year, M(SD)] – 18.60 (1.67) 16.48 (1.08) 19.30 (1.17)
Ethnicity Hana 2962 (91.6) 789 (98.0) 2173 (89.5)
Others 272 (8.4) 16 (2.0) 256 (10.5)
Residence province Hubei Province 59 (1.8) 34 (4.2) 25 (1.0)
Others 3175 (98.2) 771 (95.8) 2404 (99.0)
Residence location Urban 809 (25.0) 288 (35.8) 521 (21.4)
Town 870 (26.9) 186 (23.1) 684 (28.2)
Rural 1555 (48.1) 331 (41.1) 1224 (50.4)
Single child status Yes 652 (20.2) 130 (16.1) 522 (21.5)
No 2582 (79.8) 675 (83.9) 1907 (78.5)
History of mental disorders Yes 32 (1.0) 10 (1.2) 22 (0.9)
No 3202 (99.0) 795 (98.8) 2407 (99.1)
Chronic physical illness Yesb 387 (12.0) 115 (14.3) 272 (11.2)
No 2847 (88.0) 690 (85.7) 2157 (88.8)
Left-behind experience Yes 1658 (51.3) 369 (45.8) 1289 (53.1)
No 1576 (48.7) 436 (54.2) 1140 (46.9)
Family history of mental disorders Yes 50 (1.5) 10 (1.2) 40 (1.6)
No 3184 (98.5) 795 (98.8) 2389 (98.4)
Parental marital status Married 2847 (88.0) 709 (88.1) 2138 (88.0)
Not current marriedc 387 (12.0) 96 (11.9) 291 (12.0)
Family income (RMB per month) <3000 904 (28.0) 248 (30.8) 656 (27.0)
3000–7000 1619 (50.0) 402 (49.9) 1217 (50.1)
>7000 711 (22.0) 155 (19.3) 556 (22.9)
Relatives or friends being infected with COVID-19 Yes 26 (0.8) 10 (1.2) 16 (0.7)
No 3208 (99.2) 795 (98.8) 2413 (99.3)
Daily duration exposure to media coverage of the COVID-19 <1 h/day 2263 (70.0) 571 (70.9) 1692 (69.7)
1–4 h/day 620 (19.2) 124 (15.4) 496 (20.4)
>4 h/day 351 (10.9) 110 (13.7) 241 (9.9)
Open in a new tab
a

Han is the major ethnic group in China.

b

Chronic physical conditions referred to having at least one of arthritis, angina, asthma, diabetes, visual impairment, or hearing problems.

c

Not current married included separated, divorced, and widowed.

3.2. PLEs among adolescents during pandemic

In this sample, 51.4 % (N = 1663) adolescents reported having at least one PLE, and 11.6 % (N = 375) experienced PLEs “often” or “nearly always” during the lockdown. Compared to college students, senior high school students showed significantly more frequent PLEs (10.2 % vs. 15.9 %, χ2 = 19.38, p < 0.001). Senior high school students experienced significantly more frequent PI and PAs (p < 0.01), and numerically more frequent BEs. With regard to individual items, senior high school students also showed significantly higher prevalence on most items. Table 2 presented the prevalence of each item and factor of the CAPE-P15, as well as comparisons of prevalence of each frequent PLE between senior high school and college students.

Table 2.

Prevalence of PLEs and comparisons of prevalence of frequent PLEs between senior high school and college students.

Factors Items Prevalence (%)
 ≥Often (%)
Overall Overall Senior high school College χ2 p
PI I1_drop hints 18.1 2.1 3.1 1.8 5.24 0.022
I2_seem to be 35.6 5.8 9.1 4.7 21.24 <0.001
I3_persecuted 8.6 1.1 1.5 0.9 1.67 0.236
I4_conspiracy 6.1 0.6 1.1 0.4 5.17 0.032
I5_look oddly 13.9 2.0 3.1 1.7 6.08 0.014
Any 41.8 7.5 11.1 6.3 19.76 <0.001
BEs I6_devices 22.8 3.9 3.2 4.1 1.27 0.260
I7_thought 12.3 1.9 2.4 1.8 0.95 0.329
I8_thought own 11.5 1.8 2.6 1.5 4.43 0.035
I9_thought vivid 9.4 1.5 2.2 1.3 3.35 0.067
I10_thought echo 12.4 1.9 3.0 1.5 6.95 0.008
I11_control forc 6.5 0.7 0.9 0.7 0.24 0.627
I12_double place 5.2 0.7 1.1 0.6 2.51 0.113
Any 34.3 7.2 8.3 6.9 1.89 0.182
PAs I13_heard voices 9.6 1.4 2.0 1.2 3.14 0.076
I14_heard talking 7.0 1.2 1.6 1.0 1.79 0.181
I15_seen things 3.2 0.5 1.1 0.3 7.19 0.007
Any 11.6 1.9 3.4 1.5 11.09 0.002
Open in a new tab

Note: PI, persecutory ideation; BEs, bizarre experiences; PAs, perceptual abnormalities; PLEs, psychotic-like experiences.

3.3. Factors associated with frequent PLEs

The test of intercept variance was not statistically significant (p = 0.502). No reason for the potential multicollinearity of all variables was found, with variance inflation factor (VIF) values of 1.37 and below. As shown in Table 3 , the likelihood of experiencing frequent PLEs would increase if the adolescents were female (p < 0.001), had a history of mental disorders (p = 0.005), or chronic physical disorders (p < 0.001), had relatives or friends being infected with COVID-19 (p = 0.015), exposed to media coverage of the COVID-19 > 4 h per day, or experienced more childhood trauma (p = 0.004). Meanwhile, older age (p = 0.035), higher resilience (p < 0.001), higher level of perceived social support (p medium = 0.015, p high = 0.017), and lower childhood trauma (p medium = 0.028, p high < 0.001) were associated with decreased odds of frequent PLEs. In addition, the influential factors of the three factors (PI, BEs, PAs) were also explored with similar results (see Appendix A_Table A1).

Table 3.

Logistic regressions of influential factors of frequent PLEs among senior high school and college students.

Variables Overall
 Senior high school
 College
OR (95%CI) OR (95%CI) OR (95%CI)
Sex Male Ref Ref Ref
Female 1.77 (1.37, 2.28)⁎⁎⁎ 1.39 (0.90, 2.14) 2.22 (1.58, 3.10)⁎⁎⁎
Age – 0.93 (0.87, 0.99)⁎ 1.03 (0.85, 1.25) 0.89 (0.79, 1.02)
Ethnicity Han Ref Ref Ref
Others 1.33 (0.88, 1.99) 0.53 (0.10, 2.71) 1.45 (0.94, 2.22)
Residence province Others Ref Ref Ref
Hubei Province 1.07 (0.49, 2.31) 0.68 (0.24, 1.94) 2.11 (0.65, 6.89)
Residence location Urban Ref Ref Ref
Town 0.90 (0.66, 1.24) 0.96 (0.56, 1.65) 0.88 (0.59, 1.31)
Rural 0.82 (0.61, 1.09) 0.82 (0.40, 1.34) 0.78 (0.53, 1.14)
Single child status No Ref Ref Ref
Yes 0.96 (0.71, 1.31) 0.93 (0.52, 1.64) 1.00 (0.69, 1.46)
History of mental disorders No Ref Ref Ref
Yes 3.07 (1.40, 6.77)⁎⁎ 7.80 (1.80, 33.81)⁎⁎ 2.02 (0.73, 5.58)
Chronic physical illness No Ref Ref Ref
Yes 2.04 (1.53, 2.73)⁎⁎⁎ 1.98 (1.18, 3.30)⁎⁎ 2.06 (1.43, 2.95)⁎⁎⁎
Left behind experience No Ref Ref Ref
Yes 1.15 (0.91, 1.47) 1.00 (0.66, 1.53) 1.22 (0.90, 1.65)
Family history of mental disorders No Ref Ref Ref
Yes 1.35 (0.58, 3.15) 0.51 (0.11, 2.40) 1.99 (0.16, 1.51)
Parental marital status Married Ref Ref Ref
Not current married 1.32 (0.95, 1.84) 1.76 (0.97, 3.18) 1.23 (0.65, 6.11)
Family income (RMB per month) <3000 Ref Ref Ref
3000–7000 1.07 (0.82, 1.41) 1.22 (0.75, 1.97) 1.00 (0.71, 1.41)
>7000 1.36 (0.97, 1.92) 1.64 (0.90, 2.98) 1.22 (0.79, 1.87)
Relatives or friends being infected with COVID-19 No Ref Ref Ref
Yes 3.12 (1.25, 7.78)⁎ 5.31 (1.38, 20.45)⁎ 2.32 (0.63, 8.62)
Daily duration exposure to media coverage of the COVID-19 <1 h/day Ref Ref Ref
1–4 h/day 1.30 (0.96, 1.75) 1.64 (0.93, 2.88) 1.21 (0.85, 1.73)
>4 h/day 1.60 (1.16, 2.21)⁎⁎ 1.94 (1.14, 3.31)⁎ 1.49 (0.98, 2.27)
Resilience Low Ref Ref Ref
Medium 0.54 (0.42, 0.71)⁎⁎⁎ 0.55 (0.34, 0.91)⁎ 0.52 (0.38, 0.72)⁎⁎⁎
High 0.35 (0.24, 0.50)⁎⁎⁎ 0.46 (0.22, 0.92)⁎ 0.33 (0.21, 0.50)⁎⁎⁎
Social support Low Ref Ref Ref
Medium 0.72 (0.55–0.94)⁎ 1.36 (0.85, 2.16) 0.52 (0.38–0.73)⁎⁎⁎
High 0.63 (0.43–0.92)⁎ 1.14 (0.57, 2.26) 0.48 (0.30–0.76)⁎⁎
Childhood trauma Low Ref Ref Ref
Medium 1.54 (1.05, 2.26)⁎ 0.98 (0.51, 1.88) 1.83 (1.13, 2.97)⁎
High 3.49 (2.33, 5.22)⁎⁎⁎ 2.33 (1.18, 4.60)⁎ 4.07 (2.44, 6.79)⁎⁎⁎
Open in a new tab

Note: OR, odds ratio; CI, confidence interval.

⁎

p < 0.05.

⁎⁎

p < 0.01.

⁎⁎⁎

p < 0.001.

We also explored risk and protective factors of PLEs among senior high school and college students, respectively. Those having a history of mental disorders (p = 0.006) or chronic physical illness (p = 0.009), with relatives or friends being infected with COVID-19 (p = 0.015), exposed to media coverage of the COVID-19 > 4 h per day (p = 0.015), or having experienced more childhood trauma (p = 0.015) were at higher risk for frequent PLEs, while higher resilience (p = 0.015) exhibited as a protector in frequent PLEs among senior high school students. For college students, female individuals (p < 0.001), those having chronic physical illness (p < 0.001), or more childhood trauma (p Medium = 0.014, p high < 0.001) were more likely to experience frequent PLEs, while higher resilience (p Medium < 0.001, p high < 0.001) and more perceived social support (p Medium < 0.001, p high = 0.002) were related to lower risk of frequent PLEs.

4. Discussion

In the current study, PLEs were first assessed during the first three month of COVID-19 lockdown among the adolescence. We explored the prevalence of frequent PLEs among adolescents during COVID-19 lockdown and compared the prevalence between senior high school and college students. We also found several risk and protective factors for frequent PLEs among these two subgroups of adolescents.

The present study found that more than half of these adolescents exhibited PLEs during pandemic lockdown. The prevalence is much higher than the proportion of definite PLEs (8.1 %) among adolescents aged 12–24 in a recent study (Sullivan et al., 2020). Our data showed that 9.6 % participants experienced auditory hallucinatory experiences, which is also higher than the median prevalence among adolescents aged 13–18 years (7.5 %) (Kelleher et al., 2012). These findings suggest that the pandemic may have affected individuals' PLEs, while it cannot be ruled out that it is caused by different measures and false positive of self-report. As regard to frequent PLEs, the prevalence is also much higher than the proportion of PLEs (4.7 %, 43/910) reported in our previous research among Chinese students during the lockdown (Sun et al., 2021). The differences in the prevalence rates can partly be explained by the different time frames for the measures of PLEs, as the previous research assessed PLEs in the past month and the current study measured PLEs over a three-month period. These two studies also assessed PLEs in different populations. The previous study was conducted in colleges and technical secondary schools, both of which face similar stress from future career or academic opportunities. However, the current study was among college students and senior high school students, in which these senior high school students face totally different types of stress from college students. Additionally, different screening criteria were also adopted in these two studies. We used the cut-off value of 1.57 to screen PLEs in previous research, which have been identified for detecting PLEs within one month (Sun et al., 2022). However, in the current study, students were defined as having frequent PLEs when scoring greater than or equal to 2 on one or more items. This result also further supports the previous findings, that most PLEs are transient (75–90 %), with only a small portion persists and develops into clinical outcomes (Van Os et al., 2009).

Contrary to the findings of previous research before the pandemic outbreak (Sun et al., 2017b; Mamah et al., 2021), which found higher prevalence of PLEs in college students, our results showed that senior high school students experienced more frequent PLEs. The current study also coincides with previous studies in other mental problems during the pandemic (Zhou et al., 2020; Zhu et al., 2020), indicating that these two subgroups of students were affected by the pandemic to different degrees. Apart from the specific mental characteristics in different developmental stages, this may partly be explained by the different levels of academic burden, as senior high school students have been found to experience more educational pressures and academic burdens due to the national college entrance examination (Hou et al., 2020; Liu et al., 2020b).

Logistic regression identified several factors that increase the likelihood to experience frequent PLEs. Consistent with previous studies (Linscott and van Os, 2013; Pignon et al., 2018), we found that demographic factors, such as being female and younger age, contribute to a higher prevalence of frequent PLEs. The detection of association of frequent PLEs with history of mental disorders or chronic physical illness is also in line with previous studies (Iorfino et al., 2019). These factors should be taken into consideration when planning personalized psychological interventions during the pandemic.

COVID-19 pandemic related factors were also found to be associated with an increased risk of frequent PLEs, which is consistent with previous studies on other mental problems among adolescents during early COVID-19 outbreak period (Cao et al., 2020; Ma et al., 2020). In those studies, participants with relatives and friends being infected with COVID-19 or exposed to more media coverage of the COVID-19 are more likely to have acute stress, depression, anxiety, and insomnia, which have been found to mediate the occurrence of PLEs (Ered et al., 2018). Further studies are needed to explore the role of these mental problems in the impact of these pandemic related factors on PLEs.

Furthermore, students who tended to suffer from more childhood trauma were 1.54–3.49 times more likely to experience frequent PLEs, which confirms the conclusion of our previous study (Sun et al., 2021). In line with other literature (Grey et al., 2020; Killgore et al., 2020), resilience and social support are both found to be strong protective factors to against adverse mental health outcomes during the pandemic. Social support can not only reduce the psychological pressure but also change the attitude regarding social support and help-seeking methods during COVID-19 (Cao et al., 2020). In our previous studies, better resilience was conducive to remission of previous PLEs during the pandemic (Sun et al., 2021). Our findings have further certified their protective role in reducing the occurrence of frequent PLEs.

There were some differences in the influential factors of frequent PLEs between senior high school and college students. Gender seems to be weak associated with frequent PLEs among senior high school students. One previous survey on COVID-19 has suggested that the gender difference may not be obvious under extreme intense pressure (Su et al., 2020), which may explain the results among senior high school students who suffered from the dual stress of the pandemic and academics. Age was not associated with frequent PLEs within each subgroup. However, the difference in the prevalence of frequent PLEs between senior high school students and college students may weaken the correlation of the age variable and PLEs within the two subgroups, which is supported by one study from Ghanaian (Ahorsu et al., 2020). History of mental disorders failed to be a significant correlate of frequent PLEs among college students, which may be caused by the low prevalence of mental disorders in the subgroup (0.9 %). As to factors associated with COVID-19, such as exposure of media information and having relatives or friends being infected with COVID-19, college students seemed to be less influenced compared to senior high school students. The differences in the results between the two subgroups may be explained as generally strong ability to judge and to deal with COVID-19 correlated information among college students. Finally, higher level of perceived social support was related to less frequent PLEs of college students, while not related to frequent PLEs of high school students. This may be related to the changes in the types of social support during transition to university in Chinese students (Tao, 2000). Social support of senior high school students mainly comes from parents and teachers, while the proportion of peer support rapidly increases after entering the university, which may lead to greater differences in perceived social support among college students.

However, several potential limitations should be noted in the present study. First, the prevalence needs to be interpreted with caution. Self-reported PLEs may lead to an overestimation of the prevalence, although we have confirmed the satisfactory psychometric properties of the current CAPE-P15, to ensure the validity and reliability for assessing PLEs. Meanwhile, the convenient sampling method led to the shortage of lack of representativeness. Third, as a cross-sectional design study, limited causality could be made between these influential factors and PLEs, which needs to be addressed in a longitudinal study. Moreover, only a small percentage of participants reported having a history of mental health disorders and a family history of psychiatric illness, which is significantly lower than the prevalence of mental disorders in the recent China Mental Health Survey (Huang et al., 2019). The relatively low prevalence rates may be due to stigma or lack of mental health literacy. However, the underreported rate may have an impact on the results. Finally, data quality may become an issue when the online survey was adopted. To ensure a certain extent of data reliability, we excluded responses <5 min for analysis.

5. Conclusion

In conclusion, PLEs were relatively common among Chinese adolescents during COVID-19 lockdown, with higher prevalence among senior high school students. Multiple psychosocial and COVID-19 related factors are found to be related to frequent PLEs, which help to shed light on the characteristics of those vulnerable to mental problems during the lockdown, as well as to provide helpful information in the formulation of interventions targeting the high-risk population.

Role of the funding source

The study was supported by the National Natural Science Foundation of China (No. 82101575), Science and Technology Program of Guangzhou (No: 202102020702), Guangzhou Municipal Psychiatric Disease Clinical Transformation Laboratory (No: 201805010009), Key Laboratory for Innovation platform Plan, Science and Technology Program of Guangzhou, Science and Technology Plan Project of Guangdong Province (No. 2019B030316001) and Guangzhou Municipal Key Discipline in Medicine (2017–2019). The funding sources had no involvement in study design; the collection, analysis and interpretation of data; the writing of the report; or the decision to submit the article for publication.

CRediT authorship contribution statement

Authors Meng Sun, Dongfang Wang and Liang Zhou designed the study and wrote the protocol. All authors participated in the data collecting and provided advice on interpretation of the data. Author Dongfang Wang and Liang Zhou undertook the statistical analysis and wrote the first draft of the manuscript. Authors Chunping Chen and Meng Sun modified the manuscript. All authors contributed to and have approved the final manuscript.

Declarations of competing interest

None.

Acknowledgements

The authors want to express their sincere gratitude to all participants for participating in the study.

Appendix A. Table A1

Logistic regressions of influential factors of frequent PI, BEs, and PAs respectively.

Variables PI
 BEs
 PAs
OR (95%CI) OR (95%CI) OR (95%CI)
Sex Male Ref Ref Ref
Female 2.09 (1.52, 2.87)⁎⁎⁎ 1.20 (0.89–1.62) 0.86 (0.50, 1.48)
Age – 0.94 (0.86, 1.02) 0.98 (0.90–1.06) 0.81 (0.70, 0.95)⁎⁎
Ethnicity Han Ref Ref Ref
Others 1.54 (0.96, 2.47) 1.51 (0.95, 2.41) 2.97 (1.41, 6.28)⁎⁎
Residence province Others Ref Ref Ref
Hubei Province 1.25 (0.88, 1.79) 2.04 (0.61, 6.85) Nonea
Residence location Urban Ref Ref Ref
Town 0.90 (0.61, 1.31) 0.78 (0.53, 1.15) 0.86 (0.39, 1.91)
Rural 0.80 (0.56, 1.14) 0.83 (0.58, 1.18) 1.57 (0.81, 3.05)
Single child status No Ref Ref Ref
Yes 0.86 (0.59, 1.25) 0.81 (0.55, 1.18) 0.86 (0.44, 1.70)
History of mental disorders No Ref Ref Ref
Yes 3.02 (1.33, 6.83)⁎⁎ 2.96 (1.32, 6.67)⁎⁎ 6.43 (2.24, 18.49)⁎⁎
Chronic physical illness No Ref Ref Ref
Yes 1.77 (1.25, 2.51)⁎⁎⁎ 2.24 (1.60, 3.12)⁎⁎⁎ 1.86 (1.00, 3.46)
Left behind experience No Ref Ref Ref
Yes 1.03 (0.77, 1.38) 1.21 (0.90, 1.63) 0.64 (0.37, 1.10)
Family history of mental disorders No Ref Ref Ref
Yes 1.05 (0.43, 2.58) 0.88 (0.34, 2.26) 1.39 (0.365.38)
Parental marital status Married Ref Ref Ref
Not current married 1.75 (1.20, 2.55)⁎⁎ 1.37 (0.93, 2.03) 1.44 (0.70, 2.97)
Family income (RMB per month) <3000 Ref Ref Ref
3000–7000 1.00 (0.72, 1.39) 1.09 (0.78, 1.52) 0.66 (0.36, 1.21)
>7000 1.38 (0.92, 2.08) 1.44 (0.95, 2.18) 1.33 (0.66, 2.68)
Relatives or friends being infected with COVID-19 No Ref Ref Ref
Yes 3.64 (1.28, 10.35)⁎ 2.74 (0.91, 8.29) 6.21 (1.30, 29.53)⁎
Daily duration exposure to media coverage of the COVID-19 <1 h/day Ref Ref Ref
1–4 h/day 1.52 (1.07, 2.18)⁎ 1.02 (0.69, 1.49) 1.13 (0.56, 2.27)
>4 h/day 1.81 (1.25, 2.63)⁎⁎ 1.66 (1.15, 2.41)⁎⁎ 1.71 (0.89, 3.30)
Resilience Low Ref Ref Ref
Medium 0.55 (0.40, 0.76)⁎⁎⁎ 0.46 (0.33, 0.64)⁎⁎⁎ 0.44 (0.23, 0.84)⁎
High 0.36 (0.23, 0.57)⁎⁎⁎ 0.30 (0.19, 0.48)⁎⁎⁎ 0.46 (0.21, 3.84)⁎
Social support Low Ref Ref Ref
Medium 0.61 (0.45–0.84)⁎⁎ 0.85 (0.62, 1.18) 1.11 (0.60, 2.04)
High 0.50 (0.31–0.82)⁎⁎ 0.68 (0.42, 1.12) 1.66 (0.72, 3.83)
Childhood trauma Low Ref Ref Ref
Medium 1.38 (0.84, 2.27) 1.71 (1.03, 2.84)⁎ 3.05 (1.01, 9.22)⁎
High 3.72 (2.24, 6.19)⁎⁎⁎ 3.60 (2.12, 6.10)⁎⁎⁎ 7.70 (2.4624.07)⁎⁎⁎
Open in a new tab

Note: OR, odds ratio; CI, confidence interval.

⁎

p < 0.05.

⁎⁎

p < 0.01.

⁎⁎⁎

p < 0.001.

a

No people in this subgroup.

References

  1. Ahorsu D.K., Adjaottor E.S., Yeboah F.A., Opoku Y. Mental health challenges in academia: comparison between students of the various educational levels in Ghana. J. Ment. Health. 2020:1–8. doi: 10.1080/09638237.2020.1739253. [DOI] [PubMed] [Google Scholar]
  2. Bedford J., Enria D., Giesecke J., Heymann D.L., Ihekweazu C., Kobinger G., Lane H.C., Memish Z., Oh M.D., Sall A.A., Schuchat A., Ungchusak K., Wieler L.H. COVID-19: towards controlling of a pandemic. Lancet. 2020;395(10229):1015–1018. doi: 10.1016/S0140-6736(20)30673-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Belvederi Murri M., Zotos S., Cantarelli L., Berardi L., Curtarello E.M.A., Folesani F., Grassi L. Between China and Italy: a case report of first-episode schizophrenia in the Covid-19 era. Psychiatry Res. 2021;298 doi: 10.1016/j.psychres.2021.113804. [DOI] [PubMed] [Google Scholar]
  4. Brown E., Gray R., Lo M.S., O'Donoghue B., Nelson B., Thompson A., Francey S., McGorry P. The potential impact of COVID-19 on psychosis: a rapid review of contemporary epidemic and pandemic research. Schizophr. Res. 2020;222:79–87. doi: 10.1016/j.schres.2020.05.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cao W., Fang Z., Hou G., Han M., Xu X., Dong J., Zheng J. The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Res. 2020;287 doi: 10.1016/j.psychres.2020.112934. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Capra C., Kavanagh D.J., Hides L., Scott J. Brief screening for psychosis-like experiences. Schizophr. Res. 2013;149(1–3):104–107. doi: 10.1016/j.schres.2013.05.020. [DOI] [PubMed] [Google Scholar]
  7. Capra C., Kavanagh D.J., Hides L., Scott J.G. Current CAPE-15: a measure of recent psychotic-like experiences and associated distress. Early Interv. Psychiatry. 2017;11(5):411–417. doi: 10.1111/eip.12245. [DOI] [PubMed] [Google Scholar]
  8. Casagrande M., Favieri F., Tambelli R., Forte G. The enemy who sealed the world: effects quarantine due to the COVID-19 on sleep quality, anxiety, and psychological distress in the italian population. Sleep Med. 2020;75:12–20. doi: 10.1016/j.sleep.2020.05.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cornine A. Reducing nursing student anxiety in the clinical setting: an integrative review. Nurs. Educ. Perspect. 2020;41(4):229–234. doi: 10.1097/01.NEP.0000000000000633. [DOI] [PubMed] [Google Scholar]
  10. Ered A., Cooper S., Ellman L.M. Sleep quality, psychological symptoms, and psychotic-like experiences. J. Psychiatry Res. 2018;98:95–98. doi: 10.1016/j.jpsychires.2017.12.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Fan F., Long K., Zhou Y., Zheng Y., Liu X. Longitudinal trajectories of post-traumatic stress disorder symptoms among adolescents after the Wenchuan earthquake in China. Psychol. Med. 2015;45(13):2885–2896. doi: 10.1017/S0033291715000884. [DOI] [PubMed] [Google Scholar]
  12. Grey I., Arora T., Thomas J., Saneh A., Tohme P., Abi-Habib R. The role of perceived social support on depression and sleep during the COVID-19 pandemic. Psychiatry Res. 2020;293 doi: 10.1016/j.psychres.2020.113452. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Guessoum S.B., Lachal J., Radjack R., Carretier E., Minassian S., Benoit L., Moro M.R. Adolescent psychiatric disorders during the COVID-19 pandemic and lockdown. Psychiatry Res. 2020;291 doi: 10.1016/j.psychres.2020.113264. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. He J., Zhong X., Gao Y., Xiong G., Yao S. Psychometric properties of the chinese version of the childhood trauma questionnaire-short form (CTQ-SF) among undergraduates and depressive patients. Child Abuse Negl. 2019;91:102–108. doi: 10.1016/j.chiabu.2019.03.009. [DOI] [PubMed] [Google Scholar]
  15. Healy C., Brannigan R., Dooley N., Coughlan H., Clarke M., Kelleher I., Cannon M. Childhood and adolescent psychotic experiences and risk of mental disorder: a systematic review and meta-analysis. Psychol. Med. 2019;49(10):1589–1599. doi: 10.1017/S0033291719000485. [DOI] [PubMed] [Google Scholar]
  16. Heneka M.T., Golenbock D., Latz E., Morgan D., Brown R. Immediate and long-term consequences of COVID-19 infections for the development of neurological disease. Alzheimers Res. Ther. 2020;12(1):69. doi: 10.1186/s13195-020-00640-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Hou T.Y., Mao X.F., Dong W., Cai W.P., Deng G.H. Prevalence of and factors associated with mental health problems and suicidality among senior high school students in rural China during the COVID-19 outbreak. Asian J. Psychiatr. 2020;54 doi: 10.1016/j.ajp.2020.102305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Huang Y., Wang Y., Wang H., Liu Z., Yu X., Yan J., Yu Y., Kou C., Xu X., Lu J., Wang Z., He S., Xu Y., He Y., Li T., Guo W., Tian H., Xu G., Xu X., Ma Y., Wang L., Wang L., Yan Y., Wang B., Xiao S., Zhou L., Li L., Tan L., Zhang T., Ma C., Li Q., Ding H., Geng H., Jia F., Shi J., Wang S., Zhang N., Du X., Du X., Wu Y. Prevalence of mental disorders in China: a cross-sectional epidemiological study. Lancet Psychiatry. 2019;6:211–224. doi: 10.1016/S2215-0366(18)30511-X. [DOI] [PubMed] [Google Scholar]
  19. Iorfino F., Scott E.M., Carpenter J.S., Cross S.P., Hermens D.F., Killedar M., Nichles A., Zmicerevska N., White D., Guastella A.J., Scott J., McGorry P.D., Hickie I.B. Clinical stage transitions in persons aged 12 to 25 years presenting to early intervention mental health services with anxiety, mood, and psychotic disorders. JAMA Psychiatr. 2019;76(11):1167–1175. doi: 10.1001/jamapsychiatry.2019.2360. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Jagadheesan K., Danivas V., Itrat Q., Shekaran L., Lakra V. A 6-month study on the pattern of emergency department presentations for schizophrenia and other psychotic disorders during COVID-19 lockdown. Psychiatry Res. 2021;303 doi: 10.1016/j.psychres.2021.114081. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Kaparounaki C.K., Patsali M.E., Mousa D.V., Papadopoulou E., Papadopoulou K., Fountoulakis K.N. University students' mental health amidst the COVID-19 quarantine in Greece. Psychiatry Res. 2020;290 doi: 10.1016/j.psychres.2020.113111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kaymaz N., Drukker M., Lieb R., Wittchen H.U., Werbeloff N., Weiser M., Lataster T., Van O.J. Do subthreshold psychotic experiences predict clinical outcomes in unselected non-help-seeking population-based samples? A systematic review and meta-analysis, enriched with new results. Psychol. Med. 2012;42(11):2239–2253. doi: 10.1017/S0033291711002911. [DOI] [PubMed] [Google Scholar]
  23. Kelleher I., Cannon M. Psychotic-like experiences in the general population: characterizing a high-risk group for psychosis. Psychol. Med. 2011;41(1):1–6. doi: 10.1017/S0033291710001005. [DOI] [PubMed] [Google Scholar]
  24. Kelleher I., Connor D., Clarke M.C., Devlin N., Harley M., Cannon M. Prevalence of psychotic symptoms in childhood and adolescence: a systematic review and meta-analysis of population-based studies. Psychol. Med. 2012;42(9):1857–1863. doi: 10.1017/S0033291711002960. [DOI] [PubMed] [Google Scholar]
  25. Kepinska A.P., Iyegbe C.O., Vernon A.C., Yolken R., Murray R.M., Pollak T.A. Schizophrenia and influenza at the centenary of the 1918–1919 spanish influenza pandemic: mechanisms of psychosis risk. Front. Psychiatry. 2020;11:72. doi: 10.3389/fpsyt.2020.00072. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Killgore W., Taylor E.C., Cloonan S.A., Dailey N.S. Psychological resilience during the COVID-19 lockdown. Psychiatry Res. 2020;291 doi: 10.1016/j.psychres.2020.113216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lai J., Ma S., Wang Y., Cai Z., Hu J., Wei N., Wu J., Du H., Chen T., Li R., Tan H., Kang L., Yao L., Huang M., Wang H., Wang G., Liu Z., Hu S. Factors associated with mental health outcomes among health care workers exposed to coronavirus disease 2019. JAMA Netw. Open. 2020;3 doi: 10.1001/jamanetworkopen.2020.3976. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Linscott R.J., van Os J. An updated and conservative systematic review and meta-analysis of epidemiological evidence on psychotic experiences in children and adults: on the pathway from proneness to persistence to dimensional expression across mental disorders. Psychol. Med. 2013;43(6):1133–1149. doi: 10.1017/S0033291712001626. [DOI] [PubMed] [Google Scholar]
  29. Liu N., Zhang F., Wei C., Jia Y., Shang Z., Sun L., Wu L., Sun Z., Zhou Y., Wang Y., Liu W. Prevalence and predictors of PTSS during COVID-19 outbreak in China hardest-hit areas: gender differences matter. Psychiatry Res. 2020;287 doi: 10.1016/j.psychres.2020.112921. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Liu B., Gao F., Zhang J., Zhou H., Sun N., Li L., Liang L., Ning N., Wu Q., Zhao M. Sleep quality of students from elementary school to university: a cross-sectional study. Nat. Sci. Sleep. 2020;12:855–864. doi: 10.2147/NSS.S266493. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Lu D., Wang W., Qiu X., Qing Z., Lin X., Liu F., Wu W., Yang X., Otake Y., Luo X., Liu X. The prevalence of confirmed childhood trauma and its' impact on psychotic-like experiences in a sample of chinese adolescents. Psychiatry Res. 2020;287 doi: 10.1016/j.psychres.2020.112897. [DOI] [PubMed] [Google Scholar]
  32. Ma Z., Zhao J., Li Y., Chen D., Wang T., Zhang Z., Chen Z., Yu Q., Jiang J., Fan F., Liu X. Mental health problems and correlates among 746 217 college students during the coronavirus disease 2019 outbreak in China. Epidemiol. Psychiatr. Sci. 2020;29 doi: 10.1017/S2045796020000931. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Mamah D., Mutiso V.N., Ndetei D.M. Psychotic-like experiences among 9,564 kenyan adolescents and young adults. Psychiatry Res. 2021;302 doi: 10.1016/j.psychres.2021.113994. [DOI] [PubMed] [Google Scholar]
  34. Metel D., Arciszewska A., Daren A., Pionke R., Cechnicki A., Frydecka D., Gaweda L. Mediating role of cognitive biases, resilience and depressive symptoms in the relationship between childhood trauma and psychotic-like experiences in young adults. Early Interv. Psychiatry. 2020;14(1):87–96. doi: 10.1111/eip.12829. [DOI] [PubMed] [Google Scholar]
  35. O'Brien R.M. A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 2007;41:673–690. [Google Scholar]
  36. Odriozola-Gonzalez P., Planchuelo-Gomez A., Irurtia M.J., de Luis-Garcia R. Psychological effects of the COVID-19 outbreak and lockdown among students and workers of a spanish university. Psychiatry Res. 2020;290 doi: 10.1016/j.psychres.2020.113108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Oloniniyi I.O., Ibigbami O.I., Amiola A., Esan O.A., Esan O.O. First episode psychosis during COVID-19 pandemic: a case series. West Afr. J. Med. 2021;38(6):599–603. [PubMed] [Google Scholar]
  38. Pignon B., Peyre H., Szoke A., Geoffroy P.A., Rolland B., Jardri R., Thomas P., Vaiva G., Roelandt J.L., Benradia I., Behal H., Schurhoff F., Amad A. A latent class analysis of psychotic symptoms in the general population. Aust. N. Z. J. Psychiatry. 2018;52(6):573–584. doi: 10.1177/0004867417744255. [DOI] [PubMed] [Google Scholar]
  39. Severance E.G., Dickerson F.B., Viscidi R.P., Bossis I., Stallings C.R., Origoni A.E., Sullens A., Yolken R.H. Coronavirus immunoreactivity in individuals with a recent onset of psychotic symptoms. Schizophr. Bull. 2011;37(1):101–107. doi: 10.1093/schbul/sbp052. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Simor P., Polner B., Bathori N., Sifuentes-Ortega R., Van Roy A., Albajara S.A., Luque G.A., Benkirane O., Nagy T., Peigneux P. Home confinement during the COVID-19: day-to-day associations of sleep quality with rumination, psychotic-like experiences, and somatic symptoms. Sleep. 2021;44(7) doi: 10.1093/sleep/zsab029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Singh S., Roy D., Sinha K., Parveen S., Sharma G., Joshi G. Impact of COVID-19 and lockdown on mental health of children and adolescents: a narrative review with recommendations. Psychiatry Res. 2020;293 doi: 10.1016/j.psychres.2020.113429. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Su J., Chen X., Yang N., Sun M., Zhou L. Proximity to people with COVID-19 and anxiety among community residents during the epidemic in Guangzhou, China. BJPsych. Open. 2020;6(4) doi: 10.1192/bjo.2020.59. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Sullivan S.A., Kounali D., Cannon M., David A.S., Fletcher P.C., Holmans P., Jones H., Jones P.B., Linden D., Lewis G., Owen M.J., O'Donovan M., Rammos A., Thompson A., Wolke D., Heron J., Zammit S. A population-based cohort study examining the incidence and impact of psychotic experiences from childhood to adulthood, and prediction of psychotic disorder. Am. J. Psychiatry. 2020;177(4):308–317. doi: 10.1176/appi.ajp.2019.19060654. [DOI] [PubMed] [Google Scholar]
  44. Sun M., Hu X., Zhang W., Guo R., Hu A., Mwansisya T.E., Zhou L., Liu C., Chen X., Huang X., Shi J., Chiu H.F., Liu Z. Psychotic-like experiences and associated socio-demographic factors among adolescents in China. Schizophr. Res. 2015;166(1–3):49–54. doi: 10.1016/j.schres.2015.05.031. [DOI] [PubMed] [Google Scholar]
  45. Sun M., Xue Z., Zhang W., Guo R., Hu A., Li Y., Mwansisya T.E., Zhou L., Liu C., Chen X., Huang X., Tao H., Shi J., Liu Z., Rosenheck R. Psychotic-like experiences, trauma and related risk factors among "left-behind" children in China. Schizophr. Res. 2017;181:43–48. doi: 10.1016/j.schres.2016.09.030. [DOI] [PubMed] [Google Scholar]
  46. Sun M., Zhang W., Guo R., Hu A., Li Y., Mwansisya T.E., Zhou L., Liu C., Chen X., Tao H., Huang X., Xue Z., Chiu H.F.K., Liu Z. Psychotic-like experiences and correlation with childhood trauma and other socio-demographic factors: a cross-sectional survey in adolescence and early adulthood in China. Psychiatry Res. 2017;255:272–277. doi: 10.1016/j.psychres.2017.03.059. [DOI] [PubMed] [Google Scholar]
  47. Sun M., Wang D., Jing L., Xi C., Dai L., Zhou L. Psychometric properties of the 15-item positive subscale of the community assessment of psychic experiences. Schizophr. Res. 2020;222:160–166. doi: 10.1016/j.schres.2020.06.003. [DOI] [PubMed] [Google Scholar]
  48. Sun M., Wang D., Jing L., Zhou L. Changes in psychotic-like experiences and related influential factors in technical secondary school and college students during COVID-19. Schizophr. Res. 2021;231:3–9. doi: 10.1016/j.schres.2021.02.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Sun M., Wang D., Jing L., Yang N., Zhu R., Wang J., Chen X., Zhou L. Comparisons between self-reported and interview-verified psychotic-like experiences in adolescents. Early Interv. Psychiatry. 2022;16(1):69–77. doi: 10.1111/eip.13132. [DOI] [PubMed] [Google Scholar]
  50. Tao S. Social support during the transition to university: its characteristics and change. Psychol. Dev. Educ. 2000;1:1–5. (Press in Chinese) [Google Scholar]
  51. UNICEF Population status of children in China in 2015: facts and figures. 2018. https://www.unicef.cn/atlas-2018-cn/
  52. Van Os J., Linscott R.J., Myingermeys I., Delespaul P., Krabbendam L. A systematic review and meta-analysis of the psychosis continuum: evidence for a psychosis proneness-persistence-impairment model of psychotic disorder. Psychol. Med. 2009;39(2):179–195. doi: 10.1017/S0033291708003814. [DOI] [PubMed] [Google Scholar]
  53. Vindegaard N., Benros M.E. COVID-19 pandemic and mental health consequences: systematic review of the current evidence. Brain Behav. Immun. 2020;89:531–542. doi: 10.1016/j.bbi.2020.05.048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Wang L., Shi Z., Zhang Y., Zhang Z. Psychometric properties of the 10-item Connor-Davidson resilience scale in chinese earthquake victims. Psychiatry Clin. Neurosci. 2010;64(5):499–504. doi: 10.1111/j.1440-1819.2010.02130.x. [DOI] [PubMed] [Google Scholar]
  55. Wang Y., Wan Q., Huang Z., Huang L., Kong F. Psychometric properties of multi-dimensional scale of perceived social support in Chinese parents of children with cerebral palsy. Front. Psychol. 2017;8:2020. doi: 10.3389/fpsyg.2017.02020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Wang C., Pan R., Wan X., Tan Y., Xu L., Ho C.S., Ho R.C. Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. Int. J. Environ. Res. Public Health. 2020;17(5):1729. doi: 10.3390/ijerph17051729. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Wang D., Chen H., Zhai S., Zhu Z., Huang S., Zhou X., Pan Y., Meng D., Yang Z., Zhao J., Fan F., Liu X. Is returning to school during the COVID-19 pandemic stressful? A study on immediate mental health status of chinese college students. J. Affect. Disord. 2021;287:261–267. doi: 10.1016/j.jad.2021.03.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Wang D.F., Sun M., Jing L., Zhou L. Validity and reliability of chinese version of the 15-item positive subscale of the community assessment of psychic experiences in high school students. Chin. Ment. Health J. 2021;35:321–327. (Press in Chinese) [Google Scholar]
  59. Wang D., Zhao J., Zhai S., Ye H., Bu L., Fan F. Does sleep disturbance predicts posttraumatic stress disorder and depression among college students during COVID-19 lockdown? A longitudinal survey. Front. Public Health. 2022;10(9):86934. doi: 10.3389/fpubh.2022.986934. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. WHO . 2020. Coronavirus Disease (COVID-2019) Situation Reports 2020. [Google Scholar]
  61. Wu Z., Liu Z., Zou Z., Wang F., Zhu M., Zhang W., Tao H., Ross B., Long Y. Changes of psychotic-like experiences and their association with anxiety/depression among young adolescents before COVID-19 and after the lockdown in China. Schizophr. Res. 2021;237:40–46. doi: 10.1016/j.schres.2021.08.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Zhao X., Lan M., Li H., Yang J. Perceived stress and sleep quality among the non-diseased general public in China during the 2019 coronavirus disease: a moderated mediation model. Sleep Med. 2021;77:339–345. doi: 10.1016/j.sleep.2020.05.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Zhou S., Wang L., Yang R., Yang X., Zhang L., Guo Z., Chen J., Wang J., Chen J. Sleep problems among chinese adolescents and young adults during the coronavirus-2019 pandemic. Sleep Med. 2020;74:39–47. doi: 10.1016/j.sleep.2020.06.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Zhu X., Liu D., Yan F., Fan H., Zhao Y., Wang Z., Tan Y., Ren Y., Tan S. Psychological status of school students and employees during the COVID-19 epidemic. Chin. Ment. Health J. 2020;34:549–554. (Press in Chinese) [Google Scholar]

Articles from Schizophrenia Research are provided here courtesy of Elsevier

RESOURCES