Intention to Seek Mental Health Services During the 2022 Shanghai COVID-19 City-Wide Lockdown: Web-Based Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The implementation of COVID-19 lockdown measures had immediate and delayed psychological effects. From March 27, 2022, to June 1, 2022, the Shanghai government enforced a city-wide lockdown that affected 25 million residents. During this period, mental health services were predominantly provided through digital platforms. However, limited knowledge exists regarding the general population's intention to use mental health services during this time. OBJECTIVE: This study aimed to assess the intention of Shanghai residents to use mental health services during the 2022 Shanghai lockdown and identify factors associated with the intention to use mobile mental health services. METHODS: An online survey was distributed from April 29 to June 1, 2022, using a purposive sampling approach across 16 districts in Shanghai. Eligible participants were adults over 18 years of age who were physically present in Shanghai during the lockdown. Multivariable logistic regression was used to estimate the associations between demographic factors, lockdown-related stressors and experiences, physical and mental health status, and study outcomes-mobile mental health service use intention (mobile applications and WeChat Mini Programs [Tencent Holdings Limited]). RESULTS: The analytical sample comprised 3230 respondents, among whom 29.7% (weighted percentage; n=1030) screened positive for depression or anxiety based on the 9-item Patient Health Questionnaire or the 7-item Generalized Anxiety Disorder Scale. Less than one-fourth of the respondents (24.4%, n=914) expressed an intention to use any form of mental health services, with mobile mental health service being the most considered option (19.3%, n=728). Only 10.9% (n=440) used digital mental health services during the lockdown. Factors associated with increased odds of mobile mental health service use intention included being female, being employed, being a permanent resident, experiencing COVID-19-related stressors (such as loss of income, food insecurity, and potentially traumatic experiences), and having social and financial support. Individuals with moderate or severe anxiety, as well as those with comorbid anxiety and depression, demonstrated a higher intention to use mobile mental health services. However, individuals with depression alone did not exhibit a significantly higher intention compared with those without common mental disorders. CONCLUSIONS: Despite a high prevalence of common mental disorders among Shanghai residents, less than one-fourth of the study respondents expressed an intention to use any form of mental health services during the lockdown. Mobile apps or WeChat Mini Programs were the most considered mental health service formats. The study provided insights for developing more person-centered mobile mental health services to meet the diverse needs of different populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".