Intentions to Seek Mental Health Services During the COVID-19 Pandemic Among Chinese Pregnant Women With Probable Depression or Anxiety: Cross-sectional, Web-Based Survey Study
Bibliographic record
Abstract
BACKGROUND: Mental health problems are prevalent among pregnant women, and it is expected that their mental health will worsen during the COVID-19 pandemic. Furthermore, the underutilization of mental health services among pregnant women has been widely documented. OBJECTIVE: We aimed to identify factors that are associated with pregnant women's intentions to seek mental health services. We specifically assessed pregnant women who were at risk of mental health problems in mainland China. METHODS: A web-based survey was conducted from February to March, 2020 among 19,515 pregnant women who were recruited from maternal health care centers across various regions of China. A subsample of 6248 pregnant women with probable depression (ie, those with a score of ≥10 on the 9-item Patient Health Questionnaire) or anxiety (ie, those with a score of ≥5 on the 7-item General Anxiety Disorder Scale) was included in our analysis. RESULTS: More than half (3292/6248, 52.7%) of the participants reported that they did not need mental health services. Furthermore, 28.3% (1770/6248) of participants felt that they needed mental health services, but had no intentions of seeking help, and only 19% (1186/6248) felt that they needed mental health services and had intentions of seek help. The results from our multivariate logistic regression analysis showed that age, education level, and gestational age were factors of not seeking help. However, COVID-19-related lockdowns in participants' cities of residence, social support during the COVID-19 pandemic, and trust in health care providers were protective factors of participants' intentions to seek help from mental health services. CONCLUSIONS: Interventions that promote seeking help for mental health problems among pregnant women should also promote social support from health care providers and trust between pregnant women and their care providers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".