Development and Validation of Safe Motherhood-Accessible Resilience Training (SM-ART) Intervention to Improve Perinatal Mental Health
Bibliographic record
Abstract
Perinatal mental health issues in women can lead to a variety of health complications for both mother and child. Building resilience can strengthen coping mechanisms for pregnant women to improve their mental health and protect themselves and their children. The study aims to develop and validate the contextual and cultural appropriateness of the Safe Motherhood-Accessible Resilience Training (SM-ART) intervention for pregnant women in Pakistan. A three-phase approach was used to develop and validate an intervention that promotes resilience in pregnant women. Phase I comprised a needs assessment with stakeholders (pregnant women and key informants) to elicit opinions regarding module content. In Phase II, an intervention to build resilience was developed with the help of a literature review and formative assessment findings, and Phase III involved the validation of the intervention by eight mental health experts. The experts assessed the Content Validity Index (CVI) of the SM-ART intervention on a self-developed checklist. The resultant SM-ART intervention consists of six modules with strong to perfect CVI scores for each of the modules. Qualitative responses endorsed the strengths of the intervention as having innovative and engaging activities, contextual and cultural relevance, and a detailed, comprehensive facilitator guide. SM-ART was successfully developed and validated and is now ready for testing to promote the resilience of pregnant women at risk of perinatal mental illness.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".