Strategies to Prevent or Reduce Gender Bias in Peer Review of Research Grants: A Rapid Scoping Review
Notice bibliographique
Résumé
OBJECTIVE: To review the literature on strategies implemented or identified to prevent or reduce gender bias in peer review of research grants. METHODS: Studies of any type of qualitative or quantitative design examining interventions to reduce or prevent gender bias during the peer review of health-related research grants were included. Electronic databases including MEDLINE, EMBASE, Education Resources Information Center (ERIC), PsycINFO, Joanna Briggs, the Cochrane Library, Evidence Based Medicine (EBM) Reviews, and the Campbell Library were searched from 2005 to April 2016. A search for grey (i.e., difficult to locate or unpublished) literature was conducted and experts in the field were consulted to identify additional potentially relevant articles. Two individuals screened titles and abstracts, full-text articles, and abstracted data with discrepancies resolved by a third person consistently. RESULTS: After screening 5524 citations and 170 full-text articles, one article evaluating gender-blinding of grant applications using an uncontrolled before-after study design was included. In this study, 891 applications for long-term fellowships in 2006 were included and 47% of the applicants were women. These were scored by 13 peer reviewers (38% were women). The intervention included eliminating references to gender from the applications, letters of recommendations, and interview reports that were sent to the committee members for evaluation. The proportion of successful applications led by women did not change with gender-blinding, although the number of successful applications that were led by men increased slightly. CONCLUSIONS: There is limited research on interventions to mitigate gender bias in the peer review of grants. Only one study was identified and no difference in the proportion of women who were successful in receiving grant funding was observed. Our results suggest that interventions to prevent gender bias should be adapted and tested in the context of grant peer review to determine if they will have an impact.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,006 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».