Front. Psychiatry Frontiers in Psychiatry Front. Psychiatry 1664-0640 Frontiers Media S.A. 10.3389/fpsyt.2025.1594968 Psychiatry Systematic Review Mental health status of secondary school students: a meta-analysis of comparative studies between one-child and multi-child families in China Zhang Wei 1 2 * † Chen Pan 1 2 † Rao Shu-Ying 1 2 † Jiang Yuan-Yuan 1 2 † Su Zhaohui 3 Cheung Teris 4 Ng Chee H. 5 * Xiang Yu-Tao 1 2 * Wang Gang 6 * 1 Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao, Macao SAR, China 2 Centre for Cognitive and Brain Sciences, University of Macau, Macao, Macao SAR, China 3 School of Public Health, Southeast University, Nanjing, China 4 School of Nursing, Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China 5 Department of Psychiatry, The Melbourne Clinic and St Vincent’s Hospital, University of Melbourne, Richmond, VIC, Australia 6 Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China

Edited by: Yajun Zhao, Southwest Minzu University, China

Reviewed by: Ranran Hao, University of Chinese Academy of Sciences, China

Ruoqian Shou, Sichuan Normal University, China

*Correspondence: Wei Zhang, yc37674@connect.um.edu.mo; Chee H. Ng, cng@unimelb.edu.au; Yu-Tao Xiang, xyutly@gmail.com; Gang Wang, gangwangdoc@ccmu.edu.cn

†These authors have contributed equally to this work

 07 08 2025 2025 16 1594968 12 05 2025 07 07 2025 Copyright © 2025 Zhang, Chen, Rao, Jiang, Su, Cheung, Ng, Xiang and Wang. 2025 Zhang, Chen, Rao, Jiang, Su, Cheung, Ng, Xiang and Wang

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 Introduction

Mental health problems are common among secondary school students. However, when comparing one-child and multi-child families, the findings on the mental health of students are mixed. Therefore, we conducted a meta-analysis to compare the mental health status between secondary school students from one-child and multi-child families in China.

 Methods

Relevant studies using standard instruments on mental health (e.g., the Middle School Student Mental Health Scale; MSSMHS and the Mental Health Test; MHT) were searched in PubMed, Web of Science, PsycINFO, China National Knowledge Infrastructure (CNKI), and Wanfang. A random-effects model was employed to compute the pooled effect size. Subgroup analyses for categorical variables and meta-regression analyses for continuous variables were carried out to examine the potential moderators of group differences.

 Results

We identified 39 studies, which included 11,889 secondary school students from one-child families and 13,795 from multi-child families. No significant difference in mental health was found between students from one-child and multi-child families. However, significant group differences were observed in certain MHT domains, including Learning Anxiety [95% confidence interval (CI): -0.19; 0.00, I² = 0.0%, P = 0.04], Social Anxiety (95% CI:-0.25; 0.00, I² = 45.8%, P = 0.04), Tendency Towards Self-Blame (95% CI: -0.23; -0.07, I² = 0.0%, P < 0.01) and Allergic Tendencies (95% CI: -0.25; -0.01, I² =43.5%, P = 0.04).

 Discussion

This meta-analysis did not show significant differences in the mental health between students from one-child and multi-child families. Future research should investigate the influence of socio-demographic factors, such as gender and place of residence, on the mental health of this population.

 Systematic review registration

https://inplasy.com/wp-content/uploads/2024/03/INPLASY-Protocol-5996.pdf, identifier INPLASY202430053.

 mental health meta-analysis multi-child families one-child families secondary school students section-in-acceptance Adolescent and Young Adult Psychiatry Introduction

During a stage of rapid physical and psychological development, secondary school students often encounter mental health problems, such as anxiety and depression, which can significantly affect their academic performance and quality of life (1). High rates of mental health problems among secondary school students have been reported, for instance, 27% experienced anxiety, 24% suffered from depression, 17% developed sleep disorders, 22% engaged in self-harm behaviors, 17% had suicidal intentions and 7% made suicide plans (2). Common stressors that may increase their susceptibility to mental health problems include peer and family conflicts, social anxiety, and body image concerns (3–5). Further, secondary school students in China often confront considerable academic pressures in preparing for their ‘Zhongkao’ (Senior High School Entrance Examination), ‘Gaokao’ (National College Entrance Examination) and future career (6). Thus, research focusing on the mental health of secondary school students is crucial to address this growing challenge.

According to the Family Systems Theory (7), the behaviours of family members are usually shaped by family structure and interconnection. The Resource Dilution Theory suggests that as the number of children within the family increases, the resources for each child reduces, leading to more competition and conflict (8). In only-child families, children typically have good emotional and material support from their parents, resulting in strong parent-child relationships (9, 10). In families with siblings, children tend to compete for parental attention often resulting in sibling rivalry. According to previous research, up to 50% of children experience sibling bullying (11, 12). Although moderate sibling rivalry may help enhance social and cognitive skills (13), it can also worsen behavioral and emotional issues when it escalates to bullying.

The implementation of China’s stringent one-child policy has had profound impact on the country’s social structure and family dynamics. Initiated in the early 1980s, the one-child policy was a population control measure aimed at reducing population growth and alleviating pressures on societal resources (14). This policy restricted most families to having a single child, with some exceptions such as families from ethnic minorities or those with severely disabled children (15). However, in response to changes in socioeconomic development, the Chinese government officially changed the one-child policy at the end of 2015, allowing each family to have two children (16). This major policy shift might have considerable implications for the mental health and well-being of Chinese adolescents, which warrants in-depth research investigation.

Several validated measurement tools have been routinely employed in the assessment of mental health of children and adolescents, such as the Symptom Checklist-90 (SCL-90) that has been previously used to evaluate the mental health of secondary school students in one-child and multi-child families (17). However, as SCL-90 targets both adults and adolescents (18), it may not be sensitive enough to detect small changes in the mental health of secondary school students. Instead, a number of standardized measures have been developed explicitly for middle school students, including the Middle School Student Mental Health Scale (MSSMHS) (19) and the Mental Health Test (MHT) (20). Compared to the commonly used SCL-90 (21), MSSMHS and MHT are more specific in addressing psychological issues related to secondary school students such as academic stress and peer relationships (19, 20). In contrast, the SCL-90 is designed for a broader population (22). Additionally, MSSMHS and MHT have been validated in multiple local studies, demonstrating high reliability and validity. The test-retest reliability of MSSMHS ranged from 0.716 to 0.905 (23), and the Cronbach’s α coefficient of MHT was greater than 0.85 (24, 25), indicating good psychometric properties in measuring the mental health of secondary school students.

Previous studies on the mental health of secondary school students from one-child families in China were mostly cross-section in design and different in terms of sampling methods, selection criteria, and sample sizes (26). As a result, the findings comparing mental health status of secondary school students between one-child and multi-child families have been mixed (26, 27). Previous meta-analyses have primarily focused on the overall rates of mental health problems among secondary school students (2) or those across all age groups from both one-child and multi-child families (26).

To fill this gap, we undertook a meta-analysis to compare the mental health status of secondary school students between one-child and multi-child families in China, as well as explored their potential moderating factors.

 Materials and methods Search strategy

This systematic review and meta-analysis, registered with the number INPLASY202430053, included relevant studies published until August 28, 2023. Four researchers (WZ, PC, YYJ, and SYR) conducted an independent literature search in PubMed, Web of Science, PsycINFO, China National Knowledge Infrastructure (CNKI), and Wanfang. The detailed search strategy and search terms are listed in Supplementary Table S1 .

 Inclusion and exclusion criteria

To be eligible, participants were secondary school students from one-child families, and controls were secondary school students from multi-child households. Additionally, mental health status was measured with standardized scales specifically created for middle school students like the MHT (20) and MSSMHS (19). Cross-sectional comparative studies were adopted. Exclusion criteria included studies involving specific groups, such as ethnic minorities, and those conducted during the COVID-19 pandemic. As previously recommended (21, 28), studies conducted in specific groups and certain periods were excluded, such as, family members spending extended periods at home or during the COVID-19 pandemic, since this might increase parental stress or family conflict (29, 30). Such factors could distort the impact of family structure on children’s mental health. The four researchers independently screened the literature by reviewing titles and abstracts, and subsequently examined the full texts. Any inconsistencies encountered during the literature selection phase were addressed through consultations with the senior researcher (YTX). The literature methodology is introduced in Figure 1 .

Flow diagram of study selection procedure.

Flowchart of study selection through databases and registers. Initially, 20,459 records were identified. After removing 3,718 duplicates, 16,741 records were screened, with 16,486 excluded. Ninety-seven reports were sought, ten were not retrieved. Eighty-seven reports were assessed, forty-eight excluded for various reasons, leaving thirty-nine studies included in the review. Data extraction and study quality assessment

The data extraction of study (e.g., study title, author details, publication year, timing and location of survey, type of instruments, study design and sampling methods) and participant characteristics (e.g., mean age and type of families such as one-child and multi-child families) were performed independently by the same four researchers ( Table 1 ).

Characteristics of studies included in this meta-analysis.

No. First author and publication year Sample size (N=25,684) Standardized scale total (mean) score Region Sampling method Population Age (mean ± SD; years) Scales Quality assessment score
Total Only child (n=11,889) Non-only child (n=13,795) Only child (Mean or total) Only child (SD) Non-only child (Mean or total) Non-only child (SD)
1 Cai (2018) (31) 479 93 386 2.11 0.71 2.12 0.65 Hebei NR Senior High NR MSSMHS 6
2 Chen (2014) (32) 822 132 690 2.61 0.70 2.24 0.58 Hebei Cluster, Random Both NR MSSMHS 6
3 Cheng (2006) (33) 956 680 276 1.94 0.56 2.01 0.55 Henan Cluster Senior High 16.6 ± 1.1 MSSMHS 6
4 Feng (2013) (34) 384 319 65 127.02 38.87 137.47 38.32 Tianjin Random Junior High NR MSSMHS 5
5 Ge (2012) (35) 469 180 276 2.21 0.62 2.22 0.59 Jiangsu Random Junior High NR MSSMHS 6
6 Guo (2019) (36) 757 349 408 1.92 0.63 1.96 0.62 Sichuan Purposeful Junior High NR MSSMHS 5
7 Han (2022) (37) 1,104 235 869 1.42 0.57 1.46 0.63 Hebei Random Junior High NR MSSMHS 5
8 Huang (2017) (38) 219 46 173 2.15 0.71 2.08 0.67 Guangdong Random Senior High NR MSSMHS 7
9 Li (2001) (39) 196 172 24 36.81 9.66 36.29 9.54 Chongqing Random Junior High NR MHT 4
10 Li (2017) (40) 1,709 1,006 703 1.87 0.63 1.87 0.57 NA Stratified Junior High NR MSSMHS 6
11 Li (2021) (41) 446 87 358 2.26 0.64 2.30 0.67 Hubei NR Senior High 14.86 ± 0.52 MSSMHS 5
12 Liu (2012) (42) 393 271 122 2.16 0.57 2.11 0.57 Chongqing Random Both NR MSSMHS 6
13 Liu (2017a) (43) 392 313 79 104.65 30.14 111.39 38.09 Heilongjiang NR Junior High NR MSSMHS 5
14 Liu (2017b) (44) 825 262 563 2.31 0.57 2.31 0.54 Hunan NR Both NR MSSMHS 5
15 Liu (2020) (45) 536 93 443 2.28 0.76 1.97 0.68 Guangxi Random Junior High NR MSSMHS 6
16 Liu (2011) (46) 1,918 1,304 614 1.96 0.54 2.05 0.59 Anhui Stratified, Cluster Random Junior High NR MSSMHS 6
17 Lu (2019) (47) 904 508 395 37.66 15.92 42.52 13.98 Shandong Cluster Senior High NR MHT 6
18 Luo (2017) (48) 522 129 343 42.30 12.10 46.00 13.60 Guangdong NR Junior High NR MHT 6
19 Ma (2017) (49) 385 135 250 124.09 41.93 129.09 35.36 Hebei Cluster Junior High NR MSSMHS 6
20 Peng (2017) (50) 702 116 586 36.30 14.07 36.94 14.06 Guangdong NR Junior High NR MHT 6
21 Qiao (2016) (51) 285 148 137 106.35 32.70 102.21 30.87 Xinjiang Stratified, Cluster, Random Junior High NR MSSMHS 6
22 Qin (2019) (52) 921 113 810 2.40 0.64 2.29 0.59 Guangxi Stratified, Cluster Junior High NR MSSMHS 6
23 Shi (2010) (53) 335 84 251 2.13 0.57 2.16 0.56 Hebei Cluster, Random Senior High 17.28 ± 0.954 MSSMHS 6
24 Sun (2016) (54) 476 111 365 2.17 0.67 2.01 0.66 Hebei Random Senior High NR MSSMHS 6
25 Tang (2015) (55) 611 465 146 37.18 15.32 37.49 12.72 Shanghai Random Junior High NR MHT 5
26 Tang (2010) (56) 809 567 242 1.85 0.51 1.86 0.54 Jiangsu Cluster, Random Junior High NR MSSMHS 6
27 Tian (2011) (57) 1,863 1,084 779 1.88 0.55 1.92 0.50 Liaoning Cluster, Random Senior High 16.23 ± 0.64 MSSMHS 6
28 Wang (2018) (58) 860 445 415 2.11 0.60 2.09 0.55 Shandong NR Senior High NR MSSMHS 6
29 Wang (2022) (59) 497 284 213 1.91 0.75 1.97 0.72 Qinghai Stratified, Random Junior High 13.36 MSSMHS 6
30 Wang (2021) (60) 159 61 98 2.08 0.79 2.14 0.82 Henan NR Junior High 14.86 ± 0.533 MSSMHS 5
31 Xiang (2021) (61) 352 223 129 111.93 31.36 115.22 27.75 Xinjiang Convenient Both NR MSSMHS 6
32 Xiao (2016) (62) 654 170 461 2.07 0.46 2.15 0.52 Hubei Cluster, Random Senior High NR MSSMHS 6
33 Xie (2020) (63) 558 220 348 2.34 0.61 2.31 0.61 Hebei NR Senior High NR MSSMHS 6
34 Zhang (2012) (64) 441 215 226 1.62 0.45 1.78 0.55 Shandong NR Junior High NR MSSMHS 5
35 Zhang (2021) (65) 543 122 421 99.20 37.20 101.12 41.01 Shandong NR Junior High NR MSSMHS 4
36 Zhao (2011) (66) 285 24 261 34.58 12.28 33.68 11.39 Henan Random Senior High NR MHT 6
37 Zhao (2014) (67) 502 331 171 1.81 0.53 1.97 0.58 Jiangsu Random Junior High NR MSSMHS 6
38 Zhao (2022) (68) 395 253 142 1.85 0.59 2.00 0.64 Heilongjiang Random Senior High NR MSSMHS 5
39 Zhou (2014) (69) 1,096 539 557 2.02 0.56 2.02 0.52 Zhejiang Stratified, Cluster, Random Both NR MSSMHS 6

MHT, Mental health test; MSSMHS, Middle School Student Mental Health Scale; NR, Not reported; SD, Standard deviation.

Study quality was independently assessed by the four researchers utilizing an 8-point instrument designed for epidemiological studies (70, 71) ( Supplementary Table S1 ). Each of the items in the appraisal tool scored one point. The studies could be classified as low (0–3 points), moderate (4–6 points), or high (7–8 points) quality according to the total score (72). The agreement between researchers was above 0.8. In cases of discrepancies, consensus was reached through discussion, and any issues were resolved through consultation with another researcher (YTX).

 Statistical analysis

Data were analyzed utilizing R software (version 4.3.2) (73). A random-effects model was utilized to calculate the combined effect size, specifically the standard mean difference (SMD), along with 95% confidence intervals (CIs) for each study. The heterogeneity among studies was evaluated using the I² statistic. An I² value exceeding 50% indicated substantial heterogeneity. Subgroup analyses for categorical variables and meta-regression analyses for continuous variables were conducted to explore potential moderators of group difference. A funnel plot and Egger’s test were employed to assess publication bias. Additionally, a sensitivity analysis was conducted to ascertain the robustness and reliability of the primary results by removing studies one by one. Significance level was set at 0.05 (two-tailed test).

 Results Study characteristics

In total, 20,459 relevant publications were identified. After the removal of 3,718 duplicates, 16,417 titles and abstracts were screened, and the full text of 97 papers were assessed for eligibility ( Figure 1 ). Finally, 39 studies from across 18 provinces or municipalities in China, with 11,889 participants from one-child families and 13,795 from multi-child families were included. The mean age of the participants ranged from 13.36 to 17.28 years. Geographically, most studies were conducted in eastern China (48.7%, n = 19), followed by central (20.5%, n = 8), western China (20.5%, n = 8), and northeast China (7.7%, n = 3). All studies were cross-sectional, with 61.5% (n = 24) employing probability sampling methods.

Study quality assessment scores varied between 4 and 7, with a mean total score of 5.67; 38 studies (97.4%) were considered moderate quality, while one study (2.6%) was classified as high quality. The detailed characteristics and quality assessment scores are shown in Table 1 and Supplementary Table S2 .

 Mental health differences of children and adolescents between one-child and multi-child families

As indicated in Table 2 and Supplementary Figure S1 , the pooled SMD for mental health status score was -0.02 in terms of the MSSMHS total score (95% CI: -0.09; 0.04, I2 = 73.2%, P = 0.47), indicating no significant difference between children from one-child and those from multi-child families. In contrast, the pooled SMD for the MHT total score was -0.13 (95% CI: -0.28; 0.01, I2 = 60.0%, P = 0.06), indicating a small but statistically significant difference, although not clinically meaningful. Sub-dimension analysis further revealed that significant group differences were observed in certain MHT domains scores, including Learning Anxiety (M1) [95% confidence interval (CI): -0.19; 0.00, I² = 0.0%, P = 0.04], Social Anxiety (M2) (95% CI:-0.25; 0.00, I² = 45.8%, P = 0.04), Tendency Towards Self-Blame (M4) (95% CI: -0.23; -0.07, I² = 0.0%, P < 0.01) and Allergic Tendencies (M5) (95% CI: -0.25; -0.01, I² =43.5%, P = 0.04). In all MSSMHS domains and other MHT domains, no significant group differences were found ( Table 2 ).

Summary of the total and MSSMHS domain scores between only child and non-only child groups.

Domain No. of studies Sample size SMD 95% CI Z P Heterogeneity
Only child Non-only child Q I2 (%) P value
Global mental health 39 11,889 13,795 -0.04 -0.10; 0.02 -1.23 0.22 142.08 73.3 < 0.01
MSSMHS Total (mean) 33 10,475 12,040 -0.02 -0.09; 0.04 -0.71 0.47 119.30 73.2 <0.01
F1 32 10,213 11,477 0.00 -0.07; 0.07 0.12 0.91 144.54 78.6 <0.01
F2 32 10,213 11,477 -0.02 -0.07; 0.03 -0.77 0.44 75.26 58.8 < 0.01
F3 32 10,213 11,477 0.03 -0.07; 0.12 0.47 0.64 193.76 84.0 < 0.01
F4 32 10,213 11,477 -0.02 -0.08; 0.05 -0.46 0.65 138.51 77.6 < 0.01
F5 32 10,213 11,477 -0.03 -0.10; 0.03 -0.94 0.34 128.17 75.8 < 0.01
F6 32 10,213 11,477 -0.03 -0.09; 0.03 -1.07 0.28 99.13 68.7 < 0.01
F7 32 10,213 11,477 0.00 -0.05; 0.06 0.14 0.89 80.68 61.6 < 0.01
F8 32 10,213 11,477 -0.02 -0.07; 0.04 -0.63 0.53 82.84 62.6 < 0.01
F9 32 10,213 11,477 0.00 -0.06; 0.05 -0.23 0.82 81.25 61.8 < 0.01
F10 32 10,213 11,477 -0.03 -0.09; 0.03 -0.94 0.35 93.68 66.9 < 0.01
MHT Total (mean) 6 1,414 1,755 -0.13 -0.28; 0.01 -1.85 0.06 12.50 60.0 0.03
M1 6 1,414 1,755 -0.09 -0.19; 0.00 -2.02 0.04 4.41 0.0 0.49
M2 6 1,414 1,755 -0.13 -0.25; 0.00 -2.03 0.04 9.22 45.8 0.10
M3 6 1,414 1,755 -0.06 -0.21; 0.08 -0.86 0.39 13.91 64.1 0.02
M4 6 1,414 1,755 -0.15 -0.23; -0.07 -3.57 <0.01 1.77 0.0 0.88
M5 6 1,414 1,755 -0.13 -0.25; -0.01 -2.09 0.04 8.84 43.5 0.12
M6 6 1,414 1,755 -0.06 -0.25; 0.12 -0.68 0.50 21.45 76.7 <0.01
M7 6 1,414 1,755 -0.07 -0.24; 0.10 -0.84 0.40 15.38 67.5 <0.01
M8 6 1,414 1,755 -0.10 -0.20; 0.00 -1.91 0.06 5.99 16.6 0.31

F1, Obsessive Symptoms; F2, Paranoia; F3, Hostility; F4, Interpersonal Sensitivity; F5, Depression; F6, Anxiety; F7, Academic Stress; F8, Maladjustment; F9, Emotional Instability; F10, Psychological Imbalance; M1, Learning Anxiety; M2, Social Anxiety; M3, Tendency Towards Solitude; M4, Tendency Towards Self-Blame; M5, Allergic Tendencies; M6, Physical Symptoms; M7, Tendency Towards Fear; M8, Impulsive Tendencies; MHT, Mental health test; MSSMHS, Middle School Student Mental Health Scale; SMD, Standardized mean difference.

 Publication bias and sensitivity analyses

Both Egger’s test and funnel plot analysis did not show any significant publication bias (t = 0.97, df = 37, P = 0.34; Figure 2 and Supplementary Figure S6 ). Sensitivity analyses did not reveal any individual study that significantly altered the primary results when each was sequentially removed ( Supplementary Figure S2 ).

Funnel plots of publication bias of included studies.

Funnel plot depicting the relationship between standard error and standardized mean difference. Data points are scattered around the central dotted line, with dashed lines indicating the expected distribution range. Subgroup and meta-regression analyses

In the subgroup analysis, no significant moderators were identified. Differences in the mental health status between both groups were not significantly associated with population types (Q = 1.95, P = 0.38), geographical region (Q = 7.71, P = 0.05), publication year (Q = 0.01, P = 0.93) and the scales used (Q = 1.96, P = 0.16). In the meta-regression analyses, sample size (β = 0.00, P = 0.91), study quality (β = 0.07, P = 0.20), and age (β = 0.00, P = 0.89) were also not significantly associated with group differences in mental health status ( Table 3 ; Supplementary Figures S3-5 ).

Subgroup and meta-regression analyses of the mental health status between only child and non-only child groups.

Subgroups Categories No. of studies Sample size SMD 95% CI I2 (%) P value within subgroup Q (P value across subgroups)
Only child Non-only child
Population Senior high 13 3,805 4,610 -0.06 -0.15; 0.02 63.0 <0.01 1.95 (0.38)
Junior high 21 6,657 7,124 -0.06 -0.14; 0.01 64.5 <0.01
Both 5 1,427 2,061 0.12 -0.13; 0.37 89.1 <0.01
Publication year 2001-2014 14 5,902 4,554 -0.04 -0.16; 0.08 82.4 <0.01 0.01 (0.93)
2015-2023 25 5,987 9,241 -0.03 -0.10; 0.03 64.4 <0.01
Regions Central 7 2,588 2,631 -0.11 -0.17; -0.05 0.0 0.59 7.71 (0.05)
Northeast 3 1,650 1,000 -0.14 -0.27; -0.02 29.5 0.24
Western 8 1,653 2,286 0.07 -0.06; 0.21 66.4 <0.01
Eastern 20 4,992 7,175 -0.04 -0.14; 0.05 80.7 <0.01
Scales MSSMHS 33 10,475 12,040 -0.02 -0.09; 0.04 73.2 <0.01 1.96 (0.16)
MHT 6 1,414 1,755 -0.13 -0.28; 0.01 60.0 0.03
Meta-regression analysis
Mean No. of studies Only-child Non-only child Coefficient SE 95% CI Z P
Sample size 660.51 39 11,889 13,795 0.00 0.00 0.00; 0.00 -0.12 0.91
Study quality 5.67 39 11,889 13,795 0.07 0.05 -0.03; 0.17 1.27 0.20
age 15.53 6 2,280 1,975 0.00 0.03 -0.06; 0.05 -0.14 0.89

CI, Confidence interval; MHT, Mental health test; MSSMHS, Middle School Student Mental Health Scale; SE, Standard error; SMD, Standardized mean difference.

 Discussion

This meta-analysis of studies from 2001 to 2022 found no significant differences in mental health status between secondary school students from one-child families and multi-child families in China. These findings appear inconsistent with those of previous reviews (26, 27) using the SCL-90 that found that the mental health status of children from one-child families was better than those from multi-child families. The difference between the findings might be attributed to the use of different rating questionnaires. Additionally, the previous studies included participants from all age groups, whereas our meta-analysis targeted only secondary school students.

The lack of significant differences between one-child and multi-child families might also be explained by the changes in societal perceptions and parenting style over time regarding children from one-child families. During the initial phases of China’s one-child policy, the restrictions on having multiple children led to public resistance towards one-child families and negative stereotypes of selfishness in those growing up in one-child families (74, 75). However, as being an only child became increasingly common in China, this might have led to a shift in public perception and a reduction in discrimination against those from one-child families (26).

The heavy academic workload and intense pressure faced by Chinese secondary school students in their studies (76), apply equally to those from one-child families and multi-child families. Due to the high rates of mental health problems among secondary school students, the mental well-being of this population has been prioritized by the Chinese government (77). Many secondary schools in China have access to well-trained mental health counselors (76), mental health courses (77) and regular mental health screenings (78). Additionally, comprehensive mental health services, including mental health assessment, public education, counseling, and intervention, have been implemented in most secondary schools nationwide (79). Those with severe mental health problems would be granted a leave of absence from school, which is considered to have therapeutic benefits (80). The absence of differences in mental health between students from one-child families and those from multi-child families in this meta-analysis may reflect the effectiveness of national policies in improving the mental health of secondary school students.

In our study, students from multi-child families had higher scores on learning anxiety, social anxiety, allergic tendencies, and self-blame tendencies compared to those from one-child families. This might indicate that students from multi-child families were more prone to experience anxiety compared to their counterparts from one-child families, which is consistent with the findings of previous research using the Generalized Anxiety Disorder Scale that secondary school students from multi-child families were more likely to experience symptoms of anxiety (81). Children in multi-child families might need to compete for parental attention and care and also face more comparison and competition with their siblings. Although having siblings might provide social interactions and support, it could also create competition, jealousy, and conflict (82, 83), all of which could exacerbate their anxiety. Moreover, sibling abuse could also lead to heightened feelings of guilt and self-blame (84).

The strengths of this meta-analysis included the focus on secondary school students alone and the inclusion of studies using scales specifically developed for secondary school students, which decreased the heterogeneity of the included studies and increased the validity of the results. However, some limitations should be noted. Junior and senior secondary school students were not differentiated in most studies, despite their different physical and psychological characteristics, and potential stressors. Furthermore, previous research found significant differences in mental health status between one-child families and multi-child families among female and rural secondary school students (85). However, the data on place of residence (rural vs. urban), and gender differences between children from one-child families and multi-child families were not recorded in most studies; therefore, the influence on the results could not be examined.

In summary, this meta-analysis found no significant difference in the mental health status between secondary school students from one-child families and multi-child families, although group differences existed in certain domains. Future research should investigate the influence of socio-demographic factors, such as gender and place of residence, on the mental health of secondary school students.

 Data availability statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author/s

 Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the patients/participants OR patients/participants legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.

 Author contributions

WZ: Writing – original draft, Formal analysis, Data curation. PC: Data curation, Writing – original draft. S-YR: Writing – original draft, Data curation. Y-YJ: Writing – original draft, Data curation. ZS: Data curation, Writing – original draft. TC: Data curation, Writing – original draft. CN: Writing – review & editing, Conceptualization. Y-TX: Conceptualization, Writing – original draft. GW: Conceptualization, Writing – original draft.

 Funding

The author(s) declare that financial support was received for the research and/or publication of this article. The study was supported by Beijing High Level Public Health Technology Talent Construction Project (Discipline Backbone-01-028), the Beijing Municipal Science & Technology Commission (No. Z181100001518005), and the Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support (XMLX202128) and the University of Macau (MYRG-GRG2023-00141-FHS; CPG2025-00021-FHS).

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

 Publisher’s note

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 Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1594968/full#supplementary-material

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