Women’s participation in research platform to improve maternal, newborn and child health outcomes in West Africa
Bibliographic record
Abstract
Women participation in decision-making fora is key to ensure that their concerns are take into consideration, especially in maternal, newborn and child health issues.The objective of this study was to analyse the participation of stakeholders in the various meetings organized as part of the project "Moving for Maternal, Newborn and Child Evidence into Policy in West Africa".A gender analysis was conducted using data drawn from the attendance lists at the various meetings organized during the project implementation.This analysis showed that women were under-represented in the various meetings organized by the project, but that their profile was not different from that of men.There was a higher proportion of women among the decision-makers during the engagement, dialogue workshops and at the international workshops without significant difference.Nevertheless, in the training workshops, there was a low proportion of women among the decision-makers with statistical significant difference.The women participating in the regional platform meeting have the same profile as men in terms of decision-making power.An inequitable participation of women in the health research meetings in West Africa noted in this analysis need to be addressed in the future by the application of some innovative approaches including women as part of the organizers or by the introduction of quotas.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".