Perceptions of male partners on maternal near-miss events experienced by their female partners in Rwanda
Bibliographic record
Abstract
BACKGROUND: Maternal near-miss refers to women who survive death from life-threatening obstetric complications and has various social, financial, physical, and psychological impacts on families. OBJECTIVE: To explore male partners' perceptions of maternal near-miss experienced by their female partners and the associated psychosocial impacts on their families in Rwanda. METHODS: This was a qualitative study involving 27 semi-structured in-depth interviews with male partners whose spouses experienced a maternal near-miss event. Data were analyzed using a thematic coding to generate themes from participants' responses. RESULTS: Six key themes that emerged were: male partner's support during wife's pregnancy and during maternal near-miss hospitalization, getting the initial information about the spouse's near-miss event, psychosocial impacts of spouse's near-miss, socio-economic impact of spouse's near-miss, post- maternal near-miss family dynamics, and perceived strategies to minimize the impacts of near-miss. Male partners reported emotional, social, and economic impacts as a result of their traumatic experiences. CONCLUSIONS: The impact of maternal near-miss among families in Rwanda remains an area that needs healthcare attention. The residual emotional, financial, and social consequences not only affect females, but also their male partners and their relatives. Male partners should be involved and be well-informed about their partners' conditions and the expected long-term effects of near-miss. Also, medical and psychological follow-up for both spouses is necessary for the enhancement of the health and well-being of affected households.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".