What stakeholders think: perceptions of perinatal depression and screening in China’s primary care system
Bibliographic record
Abstract
BACKGROUND: Mental health in China is a significant issue, and perinatal depression has been recognized as a concern, as it may affect pregnancy outcomes. There are growing calls to address China's mental health system capacity issues, especially among vulnerable groups such as pregnant women due to gaps in healthcare services and inadequate access to resources and support. In response to these demands, a perinatal depression screening and management (PDSM) program was proposed. This exploratory case study identified strategies for successful implementation of the proposed PDSM intervention, informed by the Consolidated Framework for Implementation Research (CFIR) framework, in Ma'anshan city, Anhui province. METHODS: This qualitative study included four focus group discussions and two in-depth individual interviews with participants using a semi-structured interview guide. Topics examined included acceptance, utility, and readiness for a PDSM program. Participants included perinatal women and their families, policymakers, and healthcare providers. Interviews were transcribed verbatim, coded, and analyzed for emergent themes. RESULTS: The analysis revealed several promising factors for the implementation of the PDSM program including: utilization of an internet-based platform, generation of perceived value among health leadership and decision-makers, and the simplification of the screening and intervention components. Acceptance of the pre-implementation plan was dependent on issues such as the timing and frequency of screening, ensuring high standards of quality of care, and consideration of cultural values in the intervention design. Potential challenges included perceived barriers to the implementation plan among stakeholders, a lack of trained human health resources, and poor integration between maternal and mental health services. In addition, participants expressed concern that perinatal women might not value the PDSM program due to stigma and limited understanding of maternal mental health issues. CONCLUSION: Our analysis suggests several factors to support the successful implementation of a perinatal depression screening program, guidelines for successful uptake, and the potential use of internet-based cognitive behavioral therapy. PDSM is a complex process; however, it can be successfully navigated with evidence-informed approaches to the issues presented to ensure that the PDSM is feasible, effective, successful, and sustainable, and that it also improves maternal health and wellbeing, and that of their families.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".