Systematic review of the concept ‘male involvement in maternal health’ by natural language processing and descriptive analysis
Bibliographic record
Abstract
INTRODUCTION: Experts agree that male involvement in maternal health is a multifaceted concept, but a robust assessment is lacking, hampering interpretation of the literature. This systematic review aims to examine the conceptualisation of male involvement in maternal health globally and review commonly used indicators. METHODS: PubMed, Embase, Scopus, Web of Science and CINAHL databases were searched for quantitative literature (between the years 2000 and 2020) containing indicators representing male involvement in maternal health, which was defined as the involvement, participation, engagement or support of men in all activities related to maternal health. RESULTS: After full-text review, 282 studies were included in the review. Most studies were conducted in Africa (43%), followed by North America (23%), Asia (15%) and Europe (12%). Descriptive and text mining analysis showed male involvement has been conceptualised by focusing on two main aspects: psychosocial support and instrumental support for maternal health care utilisation. Differences in measurement and topics were noted according to continent with Africa focusing on HIV prevention, North America and Europe on psychosocial health and stress, and Asia on nutrition. One-third of studies used one single indicator and no common pattern of indicators could be identified. Antenatal care attendance was the most used indicator (40%), followed by financial support (17%), presence during childbirth (17%) and HIV testing (14%). Majority of studies did not collect data from men directly. DISCUSSION: Researchers often focus on a single aspect of male involvement, resulting in a narrow set of indicators. Aspects such as communication, shared decision making and the subjective feeling of support have received little attention. We believe a broader holistic scope can broaden the potential of male involvement programmes and stimulate a gender-transformative approach. Further research is recommended to develop a robust and comprehensive set of indicators for assessing male involvement in maternal health.
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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 | 0.041 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".