Dataset on the effects of psychological care on depression and suicide ideation in underrepresented children.

Massive increases in the risks of depressive disorders and the ensuing suicide have become the overarching menace for children/adolescents. Despite global consensus to instigate psychological healthcare policy for these children/adolescents, their effects remain largely unclear neither from a small amount of official data nor from small-scale scientific studies. More importantly, in underprivileged children/adolescents in lower-middle-economic-status countries/areas, the data collection may not be as equally accessible as in developed countries/areas, thus resulting in underrepresented observations. To address these challenges, we released a large-scale and multi-center cohort dataset (n = 249,772) showing the effects of primary psychological healthcare on decreasing depression and suicidal ideation in these children/adolescents who were underrepresented in previous studies or current healthcare systems, including unattended children/adolescents, orphans, children/adolescents in especially difficult circumstances, and "left-behind" and "single-parenting" children/adolescents. We provided all individual data recording the depressive symptoms and suicide ideation that had been collected at baseline (Oct 2022) and half-year follow-up (May 2023) from practicing this psychological healthcare system.

Cortical gradient perturbation in attention deficit hyperactivity disorder correlates with neurotransmitter-, cell type-specific and chromosome- transcriptomic signatures.

AIMS: This study aimed to illuminate the neuropathological landscape of attention deficit hyperactivity disorder (ADHD) by a multiscale macro-micro-molecular perspective from in vivo neuroimaging data. METHODS: The "ADHD-200 initiative" repository provided multi-site high-quality resting-state functional connectivity (rsfc-) neuroimaging for ADHD children and matched typically developing (TD) cohort. Diffusion mapping embedding model to derive the functional connectome gradient detecting biologically plausible neural pattern was built, and the multivariate partial least square method to uncover the enrichment of neurotransmitomic, cellular and chromosomal gradient-transcriptional signatures of AHBA enrichment and meta-analytic decoding. RESULTS: Compared to TD, ADHD children presented connectopic cortical gradient perturbations in almost all the cognition-involved brain macroscale networks (all pBH <0.001), but not in the brain global topology. As an intermediate phenotypic variant, such gradient perturbation was spatially enriched into distributions of GABAA/BZ and 5-HT2A receptors (all pBH <0.01) and co-varied with genetic transcriptional expressions (e.g. DYDC2, ATOH7, all pBH <0.01), associated with phenotypic variants in episodic memory and emotional regulations. Enrichment models demonstrated such gradient-transcriptional variants indicated the risk of both cell-specific and chromosome- dysfunctions, especially in enriched expression of oligodendrocyte precursors and endothelial cells (all pperm <0.05) as well enrichment into chromosome 18, 19 and X (pperm <0.05). CONCLUSIONS: Our findings bridged brain macroscale neuropathological patterns to microscale/cellular biological architectures for ADHD children, demonstrating the neurobiologically pathological mechanism of ADHD into the genetic and molecular variants in GABA and 5-HT systems as well brain-derived enrichment of specific cellular/chromosomal expressions.

Altered EEG Microstates Dynamics in Individuals with Subthreshold Depression When Generating Negative Future Events.

Negative bias in prospection may play a crucial role in driving and maintaining depression. Recent research suggests abnormal activation and functional connectivity in regions of the default mode network (DMN) during future event generation in depressed individuals. However, the neural dynamics during prospection in these individuals remain unknown. To capture network dynamics at high temporal resolution, we employed electroencephalogram (EEG) microstate analysis. We examined microstate properties during both positive and negative prospection in 35 individuals with subthreshold depression (SD) and 35 controls. We identified similar sets of four canonical microstates (A-D) across groups and conditions. Source analysis indicated that each microstate map partially overlapped with a subsystem of the DMN (A: verbal; B: visual-spatial; C: self-referential; and D: modulation). Notably, alterations in EEG microstates were primarily observed in negative prospection of individuals with SD. Specifically, when generating negative future events, the coverage, occurrence, and duration of microstate A increased, while the coverage and duration of microstates B and D decreased in the SD group compared to controls. Furthermore, we observed altered transitions, particularly involving microstate C, during negative prospection in the SD group. These altered dynamics suggest dysconnectivity between subsystems of the DMN during negative prospection in individuals with SD. In conclusion, we provide novel insights into the neural mechanisms of negative bias in depression. These alterations could serve as specific markers for depression and potential targets for future interventions.

A feasibility study of goal-directed network-based real-time fMRI neurofeedback for anhedonic depression.

Anhedonia is a hallmark symptom of depression that often lacks adequate interventions. The translational gap remains in clinical treatments based on neural substrates of anhedonia. Our pilot study found that depressed individuals depended less on goal-directed (GD) reward learning (RL), with reduced reward prediction error (RPE) BOLD signal. Previous studies have found that anhedonia is related to abnormal activities and/or functional connectivities of the central executive network (CEN) and salience network (SN), both of which belong to the goal-directed system. In addition, it was found that real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) could improve the balance between CEN and SN in healthy individuals. Therefore, we speculate that rt-fMRI NF of the CEN and SN associated with the GD system may improve depressive and/or anhedonic symptoms. Therefore, this study (1) will examine individuals with anhedonic depression using GD-RL behavioral task, combined with functional magnetic resonance imaging and computational modeling to explore the role of CEN/SN deficits in anhedonic depression; and (2) will utilize network-based rt-fMRI NF to investigate whether it is feasible to regulate the differential signals of brain CEN/SN of GD system through rt-fMRI NF to alleviate depressive and/or anhedonic symptoms. This study highlights the need to elucidate the intervention effects of rt-fMRI NF and the underlying computational network neural mechanisms.

The Ripple Effect: Unveiling the Bidirectional Relationship Between Negative Life Events and Depressive Symptoms in Medical Cadets.

Background: Previous studies have explored the relationship between negative life events and depression, but little is known about the bidirectional relationship between negative life events and depression, particularly in specific groups of medical cadets. Purpose: This study aimed to explore the relationship between negative life events and depressive symptoms among medical cadets during their four years of college. Methods: An analysis of 4-wave longitudinal data collected from 2015-2018 was conducted using a cross-lagged panel network (CLPN) model to explore the complex causal relationship between negative life events and depressive symptoms in medical cadets (N=433). Results: We found differences in negative life events and depressive symptoms among medical cadets across four network models over four years of university. Nodes A-21, A-20, A-23 and A-24, and depressive symptoms D-6 showed greater lagged effect values. Conclusion: Our findings suggest that there is a lagged and mutually causal interaction between negative life events and depressive symptoms in medical cadets over 4 years of college, but that the predictability of negative life events is more important. However, more attention needs to be paid to the predictive role of depressive symptoms, especially those in early life which are often overlooked. Our study provides new insights into the relationship between negative life events and depressive symptoms in university students and helps to refine strategies for prevention and intervention of depression.

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.

BACKGROUND: The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS: Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS: A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (ß = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS: Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

Risk factors and prediction model for mental health in Chinese soldiers.

Introduction: This study aimed to explore potential risk factors for mental health concerns, and the prediction model for mental health concerns in Chinese soldiers was constructed through combined eligible risk factors. Methods: This cross-sectional study was performed on soldiers under direct command from Gansu, Sichuan, and Chongqing in China, and the soldiers were selected by cluster convenient sampling from 16 October 2018 to 10 December 2018. The Symptom Checklist-90 (SCL-90) and three questionnaires (Military Mental Health Status Questionnaire, Military Mental Health Ability Questionnaire, and Mental Quality Questionnaire for Army Men) were administered, including demographics, military careers, and 18 factors. Results: Of 1,430 Chinese soldiers, 162 soldiers presented mental disorders, with a prevalence of 11.33%. A total of five risk factors were identified, including serving place (Sichuan vs. Gansu: OR, 1.846, 95% CI: 1.028-3.315, P = 0.038; Chongqing vs. Gansu: OR, 3.129, 95% CI, 1.669-5.869, P = 0.003), psychosis (OR, 1.491, 95% CI, 1.152-1.928, P = 0.002), depression (OR, 1.482, 95% CI, 1.349-1.629, P < 0.001), sleep problems (OR, 1.235, 95% CI, 1.162-1.311, P < 0.001), and frustration (OR, 1.050, 95% CI, 1.015-1.087, P = 0.005). The area under the ROC curve by combining these factors was 0.930 (95% CI: 0.907-0.952) for predicting mental disorders in Chinese soldiers. Conclusion: The findings of this study demonstrate that mental disorders and onset in Chinese soldiers can be predicted on the basis of these three questionnaires, and the predictive value of the combined model was high.

Association of burnout with depression in pharmacists: A network analysis.

Background: Burnout and depression have overlapping symptoms, but the extent of overlap remains unclear, and the complex relationship between burnout and depression in pharmacists is rarely explored. Methods: We investigated burnout and depression in 1,322 frontline pharmacists, and explored the complex relationship between burnout and depression in those pharmacists using network analysis. Results: Network analysis showed that there were 5 communities. A partial overlap was found between burnout and depressive symptoms in pharmacists. The nodes MBI-6 (I have become more callous toward work since I took this job), D18 (My life is meaningless), and D10 (I get tired for no reason) had the highest expected influence value. D1 (I feel down-hearted and blue) and D14 (I have no hope for the future) were bridge symptoms connected with emotional exhaustion and reduced professional efficacy, respectively. Conclusion: A partial overlap exists between burnout and depressive symptoms in pharmacists, mainly in the connection between the emotional exhaustion and reduced professional efficacy and the depressive symptoms. Potential core targets identified in this study may inform future prevention and intervention.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.

Importance: Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. Objective: To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. Evidence Review: PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. Findings: A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). Conclusions and Relevance: This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

Microbiota alteration of Chinese young male adults with high-status negative cognitive processing bias.

Introduction: Evidence suggests that negative cognitive processing bias (NCPB) is a significant risk factor for depression. The microbiota-gut-brain axis has been proven to be a contributing factor to cognitive health and disease. However, the connection between microbiota and NCPB remains unknown. This study mainly sought to explore the key microbiota involved in NCPB and the possible pathways through which NCPB affects depressive symptoms. Methods: Data in our studies were collected from 735 Chinese young adults through a cross-sectional survey. Fecal samples were collected from 35 young adults with different levels of NCPB (18 individuals were recruited as the high-status NCPB group, and another 17 individuals were matched as the low-status NCPB group) and 60 with different degrees of depressive symptoms (27 individuals were recruited into the depressive symptom group, as D group, and 33 individuals were matched into the control group, as C group) and analyzed by the 16S ribosomal RNA sequencing technique. Results: As a result, the level of NCPB correlated with the degree of depressive symptoms as well as anxiety symptoms and sleep quality (p < 0.01). The ß-diversity of microbiota in young adults was proven to be significantly different between the high-status NCPB and the low-status NCPB groups. There were several significantly increased bacteria taxa, including Dorea, Christensenellaceae, Christe -senellaceae_R_7_group, Ruminococcaceae_NK4A214_group, Eggerthellaceae, Family-XIII, Family_XIII_AD3011_group, Faecalibaculum, and Oscillibacter. They were mainly involved in pathways including short-chain fatty acid (SCFA) metabolism. Among these variable bacteria taxa, Faecalibaculum was found associated with both NCPB and depressive symptoms. Furthermore, five pathways turned out to be significantly altered in both the high-status NCPB group and the depressive symptom group, including butanoate metabolism, glyoxylate and dicarboxylate metabolism, propanoate metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, valine, leucine, and isoleucine degradation. These pathways were related to SCFA metabolism. Discussion: Fecal microbiota is altered in Chinese young male adults with high status NCPB and may be involved in the biochemical progress that influences depressive symptoms.

Social anxiety and depression symptoms in Chinese left-behind children after the lifting of COVID-19 lockdown: A network analysis.

BACKGROUND: Returning to social life after the lifting of COVID-19 lockdown may increase risk of social anxiety, which is highly co-morbid with depression. However, few studies have reported the association between them. AIMS: To explore the complex relationship between social anxiety and depression symptoms in left-behind children after the lifting of the COVID-19 lockdown. METHODS: A cross-sectional survey was conducted 6 months after the lockdown removal. A total of 3,107 left-behind children completed the survey with a mean age of 13.33 and a response rate of 87.77%. Depression and social anxiety severity were assessed by the DSM-5 Patient Health Questionnaire for Adolescents and the DSM-5 Social Anxiety Disorder Questionnaire, respectively. The symptom-level association between the two disorders was examined using network analysis. RESULTS: After the lifting of COVID-19 lockdown, the prevalence of depression and social anxiety in left-behind children was 19.57% and 12.36%, respectively, with a co-morbidity rate of 8.98%. Network analysis showed that "Social tension" and "Social avoidance" had the greatest expected influence; "Humiliation" and "Motor" were bridge symptom nodes in the network. The directed acyclic graph indicated that "Social fright" was at the upstream of all symptoms. CONCLUSION: Attention should be paid to social anxiety symptoms in left-behind children after the lifting of COVID-19 lockdown. Prevention and intervention measures should be taken promptly to reduce the comorbidity of social anxiety and depression symptoms in the left-behind children after the lifting of lockdown.

The development of the Negative Bias in Prospection Scale: A novel assessment of dysfunctional prospection in depression.

"Prospection" refers to the ability to mentally simulate one's future episodes. Negative bias in prospection, which includes both reduced positivity and enhanced negativity, is considered to be closely related to the development and maintenance of depression. However, there is a relative paucity of measures that adequately assess negative bias in prospection in depressed people. The current study developed and initially validated the Negative Bias in Prospection Scale (NBPS) using a nonclinical sample (n = 959). The validity and reliability of the NBPS were investigated in several ways. Results suggested the 14 NBPS items loaded onto three factors, namely the "Increased negativity," the "Reduced positivity," and the "Overgeneralization." The NBPS demonstrated decent internal consistency and test-retest reliability. Further, the NBPS was strongly associated with severity of depressive symptoms. Moreover, the NBPS was correlated in a theoretically meaningful way with other measures of future-oriented cognitions or negative biases. In conclusion, the NBPS has promising preliminary psychometric properties. It will be an efficient tool for exploring dysfunctional prospection in depression and provides a novel measure for mechanism of change in prospection-based interventions.

Life events and depression among children and adolescents in southwest China: a two-stage moderated mediation model of social support and cognitive styles.

BACKGROUND: According to data from the National Health Commission in 2018, more than 30 million children and adolescents in China suffered from mental health problems of varying degrees, with depression accounting for the largest proportion. Life events occur at every stage of child and adolescent development. Many studies have found a relationship between life events and depression in children and adolescents, but few studies have further explored the mediating and moderating variables that influence this relationship. Based on theoretical and empirical research on social support and cognitive styles, this study established a two-stage moderated mediating model to test whether social support mediates life events to depression, and cognitive style moderates the two mediating pathways. METHODS: We recruited 3540 participants from primary and secondary schools in Chongqing and collected 2814 valid data. All the participants completed self-report measures of life events, depression, social support and cognitive styles. The moderated mediation model was examined using SPSS PROCESS model 58. RESULTS: (1) There were significant positive correlations between life events and depression. (2) Social support mediates the relationship between life events and depression in children and adolescents. (3) Cognitive style moderates life events to social support and social support to depression. LIMITATIONS: This is a cross-sectional study and the questionnaire is self-reported. CONCLUSIONS: In children and adolescents, life events can influence depression through the mediating role of social support and cognitive styles could moderate its two mediating pathways.

Adjusting intervention strategies for mental health of COVID-19 patients: A network analysis based on a survey in Omicron-infected patients.

Background: The COVID-19 pandemic had a major impact on people's mental health. As the SAS-Cov-2 evolves to become less virulent, the number of asymptomatic patients increases. It remains unclear if the mild symptoms are associated with mild perceived stress and mental illness, and the interventions to improve the mental health of the patients are rarely reported. Methods: This cross-sectional study investigated the level of depression, anxiety and perceived stress of 1,305 COVID-19 patients who received treatment in the Fangcang shelter hospitals in Shanghai, China. Network analysis was used to explore the relationship among depression, anxiety and perceived stress. Results: The prevalence of depression, anxiety and perceived stress in the patients with Omicron infection were 9.03, 4.60, and 17.03%, respectively, lower than the prevalence reported during the initial outbreak of COVID-19. "Restlessness (A5)," "Uncontrollable worry (A2)," "Trouble relaxing (A4)" and "Fatigue (D4)" had the highest expected influence values. "Irritability (A6)" and "Uncontrollable (S1)" were bridge symptoms in the network. Comparative analysis of the network identified differences in the network structures between symptomatic and asymptomatic patients. Conclusion: This study investigated the prevalence of depression, anxiety and perceived stress and the correlation among them in Omicron-infected patients in Fangcang shelter hospital, in Shanghai, China. The core symptoms identified in the study provide insight into targeted clinical prevention and intervention of mental health in non-severe Omicron-infected patients.

The Relationships between Prospection, Self-Efficacy, and Depression in College Students with Cross-Lagged Analysis.

Depression is one of the most prevalent and disabling mental health problems in college students. Previous studies have established cross-sectional associations between negative bias in prospection e.g., increased negativity in future simulation, low self-efficacy, and depressive symptoms. Nevertheless, the temporal bidirectional associations between them are rarely examined. In the current study, we collected valid data on 276 college students at two time points within a 10 week interval. Cross-lagged panel analysis was applied to investigate the relationships between proportions of negative future events, levels of self-efficacy, and depressive symptoms. Results suggested depressive symptoms predict subsequent proportions of negative prospections and levels of self-efficacy. Inversely, neither prospection nor self-efficacy predicted depression. Temporal correlations between prospection and self-efficacy were also not significant. Since this is one of the first studies that attempts to figure out temporal links between these mutually informing factors, more longitudinal research is needed to draw a firm conclusion. This study provides new insights into the relationship between negative biases in cognitions and depressive symptoms and highlights the need to intervene early with depressive symptoms before any possible cognitive distortions in college students.

Revision and psychometric properties of the negative cognitive processing bias scale.

Negative cognitive processing bias (NCPB) is a cognitive trait that makes individuals more inclined to prioritize negative external stimuli (cues) when processing information. Cognitive biases have long been observed in mood and anxiety disorders, improving validation of tools to measure this phenomenon will aid us to determine whether there is a robust relationship between NCPB and major depressive disorder, anxiety disorders and other clinical disorders. Despite the development of an initial measure of this trait, that is, the negative cognitive processing bias questionnaire (NCPBQ), the lack of psychometric examinations and applications in large-scale samples hinders the determination of its reliability and validity and further limits our understanding of how to measure the NCPB traits of individuals accurately. To address these issues, the current study evaluated the psychometric properties of the NCPBQ in a large-scale sample (n = 6,069), which was divided into two subsamples (Subsample 1, n = 3,035, serving as the exploratory subsample, and Subsample 2, n = 3,034, serving as the validation subsample), and further revised it into a standardized scale, that is the negative cognitive processing bias scale (NCPBS), based on psychometric constructs. The results show that NCPBS possesses good construct reliability, internally consistent reliability, and test-retest reliability. Furthermore, by removing two original items from NCPBQ, NCPBS was found to have good criterion-related validity. In conclusion, the present study provides a reliable and valid scale for assessing negative cognitive processing bias of individuals.

Attitude and Performance for Online Learning during COVID-19 Pandemic: A Meta-Analytic Evidence.

The COVID-19 pandemic prominently hit almost all the aspects of our life, especially in routine education. For public health security, online learning has to be enforced to replace classroom learning. Thus, it is a priority to clarify how these changes impacted students. We built a random-effect model of a meta-analysis to pool individual effect sizes for published articles concerning the attitudes and performance towards online learning. Databases included Google Scholar, PubMed and (Chinese) CNKI repository. Further, a moderated analysis and meta-regression were further used to clarify potential heterogenous factors impacting this pooled effect. Forty published papers (n = 98,558) were screened that were eligible for formal analysis. Meta-analytic results demonstrated that 13.3% (95% CI: 10.0-17.5) of students possessed negative attitudes towards online learning during the COVID-19 pandemic. A total of 12.7% (95% CI: 9.6-16.8) students were found to report poor performance in online learning. Moderated analysis revealed poor performance in online learning in the early pandemic (p = 0.006). Results for the meta-regression analysis showed that negative attitudes could predict poor learning performance significantly (p = 0.026). In conclusion, online learning that is caused by COVID-19 pandemic may have brought about negative learning attitudes and poorer learning performance compared to classroom learning, especially in the early pandemic.

Development and validation of police mental health ability scale.

OBJECTIVES: Police officers are generally under long-term occupational stress. Good mental health ability enables them to better deal with emergencies and enhance their combat effectiveness. We aimed to develop the Police Mental Health Ability Scale (PMHAS) to provide a reference for police selection and ability training. METHODS: Through literature analysis, individual interviews, half-open and half-closed questionnaire surveys, and expert consultations, the components of police mental health ability (PMHA) were theoretically constructed. Then, we enrolled 824 in-service police officers who participated in the training in Chongqing City and Sichuan Province from November 2018 to January 2019 and recovered 767 valid questionnaires (recovery rate, 93.08%). RESULTS: Exploratory factor analysis generated five factors for PMHAS, including cognitive intelligence, emotional catharsis, swift decisiveness, behavioral drive, and reward pursuit, accounting for 58.904% of the variance. Confirmatory factor analysis demonstrated that the model fit well (χ2/df = 1.117, RMSEA = 0.020, GFI = 0.948, CFI = 0.990, IFI = 0.990, TLI = 0.987). The correlation coefficients of factors (r = -0.023 ~ 0.580) were lower than that of each factor and total score (r = 0.477 ~ 0.819). The Cronbach's α coefficients of PMHAS and its factors were 0.606-0.863, and the test-retest reliabilities were 0.602-0.732. CONCLUSION: These results suggest that PMHAS is reliable and valid enough for measuring PMHA, which shows that it is a potentially valuable tool for assessing the mental health ability of police officers.

Impulsiveness indirectly affects suicidal ideation through depression and simultaneously moderates the indirect effect: A moderated mediation path model.

Objective: This study aims to investigate the indirect effect of impulsiveness on suicidal ideation through depression and the moderating effect of impulsiveness on the indirect effect in an integrated path model. Methods: Self-rating depression scale (SDS), Barratt impulsiveness scale-11th version (BIS-11), and self-rating idea of suicide scale (SIOSS) were applied. A moderated mediation path model was established including impulsiveness, depression, and suicidal ideation as observed variables. Results: The main results revealed that the moderated mediation path model fit well in describing the relationships among impulsiveness, depression, and suicidal ideation. The indirect effect of impulsiveness mediated by depression and the moderating effect of impulsiveness on suicidal ideation was significant. Multiple comparisons showed that the indirect effects under different conditions of impulsiveness had statistical differences. The higher the impulsiveness was, the stronger the predictive effect of depression on suicidal ideation was. Conclusions: The present study confirms that people who have impulsive traits are riskier to generate suicidal thoughts because they are more likely to suffer from depression and that people who are depressive have even higher risk to develop suicidal thoughts when they simultaneously have impulsive traits. In clinical and health care work, when considering depression to prevent suicidal ideation, impulsiveness needs to be monitored throughout the process of premorbid and onset stages of depression.