‘She mustn’t be the mother alone’: motivators, barriers, and recommendations for men’s engagement with reproductive research in South Africa
Bibliographic record
Abstract
There is a notable lack of engagement in reproductive and lifecourse research with and from men, with a disproportionate focus on women. While studies investigating the lack of men's engagement with services exist, there is little evidence on their engagement with reproductive and lifecourse research. This article qualitatively examines men's engagement in such research in the Bukhali trial in South Africa. Single, in-depth interviews were conducted with 19 male partners of Bukhali participants, and data were thematically analysed. Results were grouped into three domains: (i) motivators for engagement, (ii) barriers to engagement, and (iii) recommendations to improve engagement. Motivators to engage included partner support, desiring to learn and gain knowledge, and taking the opportunity to access the same perceived trial benefits as their partner. Barriers included having misconceptions around the research, fear and mistrust of hospitals and illness, gendered ideas around 'women's health issues', hesitance around sharing personal details, and lack of time and perceived (financial) benefits. To increase engagement, male partners recommended increasing awareness and creating safe research spaces for men. Each theme was inflected with male partners' sociocultural and contextual realities and provided valuable insights into obstacles and opportunities for improving men's engagement in reproductive research.
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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 | 0.077 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".