Favoriser la participation des hommes en recherche : une revue narrative des stratégies gagnantes
Bibliographic record
Abstract
INTRODUCTION: Over the past two decades, research on men's lived experiences has significantly increased. However, their voices remain underrepresented in areas such as reproduction and parenting. This raises questions about their willingness to participate in studies targeting them and the effectiveness of researchers' methods to engage them. This article aims to describe various strategies to enhance men's engagement in research. METHOD: A narrative review was conducted by consulting the CINAHL, MEDLINE, and Cairn-info databases, as well as the Sofia search tool. Additional publications were identified using the snowball method and reverse citation tracking. RESULTS: 31 publications were selected and analyzed. Effective strategies for engaging men were gathered and explained according to three key stages: recruitment, maintaining engagement, and data collection. DISCUSSION: This review highlights the lack of concrete methodological strategies to anticipate and overcome obstacles in studies involving men. Increased transparency of methodological aspects in future publications could improve knowledge and practices for mobilizing men in 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 | 0.109 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".