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Enregistrement W7115697565 · doi:10.48448/y2ts-yj94

Assessment of an Intervention to Equalize the Proportion of Funded Grant Applications for Underrepresented Groups at the Canadian Institutes of Health Research

2025· other· W7115697565 sur OpenAlexaffabout

Notice bibliographique

RevueUnderline Science Inc. · 2025
Typeother
Langue
Domaine
Thématique
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésGrant fundingPsychological interventionPrincipal (computer security)Intervention (counseling)Underrepresented MinorityHealth services researchProgram evaluationOriginal research

Résumé

récupéré en direct d'OpenAlex

Anne Lasinsky,<sup>1</sup> James Wrightson,<sup>2</sup> Matthew Hogel,<sup>3</sup> Alannah Brown,<sup>3</sup> Adrian Mota,<sup>3</sup> Karim M. Khan,<sup>1,2,4</sup> Clare L. Ardern<sup>5,6</sup> <h4>Objective</h4> A small number of Canadian research funders have implemented interventions to address known biases in grant peer review. Equalization, which aims to match the proportion of funded grant applications to the proportion of submitted applications from specific underrepresented groups, is one such intervention. In 2016, the Canadian Institutes of Health Research (CIHR) implemented equalization for early career researcher (ECR) principal applicants in its Project Grant Competition. In 2021, equalization expanded to female principal applicants and French-language applicants. The objective of this study was to describe the outcome of equalization in 2022. <h4>Design</h4> This was a retrospective analysis of the number of funded grants in the spring 2022 CIHR Project Grant Competition. The equalization intervention was applied to applications submitted by ECR applicants, female principal applicants, and applications in French. To apply the intervention, first, scores for all applications submitted to the Project Grant Competition, which were reviewed by 59 committees, were converted to a percentage rank to account for scoring differences across committees. CIHR funded applications in rank order from the top percent rank, as far down as the competition budget allowed, and intervened to ensure that the next-ranked applications from each underrepresented applicant group were funded. Equalization matched the proportion of applications funded to the proportion of applications submitted by each group. No grants were defunded. Any grants that were equalized were added to the pool of funded grants—they did not displace another applicant’s score-win. This study descriptively analyzed routinely collected data from CIHR. The main outcome was the number of grant applications funded for each underrepresented applicant group, with and without equalization. The secondary outcome was the proportion of funded applications for each underrepresented applicant group, with and without equalization. <h4>Results</h4> There were 2095 applications submitted to the spring 2022 Project Grant Competition. Before equalization, 370 applications (17.7%) were funded. After equalization, 405 applications (19.3%) were funded with a total of CAD$325 million. ECR principal applicants submitted 580 applications (27.7%), female principal applicants submitted 774 applications (36.9%), and 25 applications (1.2%) were submitted in French). After equalization, 21 additional applications from ECR principal applicants and 22 applications from female principal applicants were funded (<b>Table 25-1092</b>). The funding success rates increased from 16.9% to 20.7% for ECR principal applicants and 17.5% to 20.3% for female principal applicants. One additional French-language application was funded with equalization; the success rate for French-language applicants increased from 17.4% to 21.7%. https://assets.underline.io/markdown_image/1/image/7ed3f1babc4b418643dbb1605b2d00b4.png <h4>Conclusions</h4> In the spring 2022 CIHR Project Grant Competition, equalization increased the number of health research grants awarded and the funding success rate for ECR and female principal applicants, and for applications submitted in French. <h4>Affiliations</h4> <sup>1</sup>School of Kinesiology, The University of British Columbia, Vancouver, Canada; <sup>2</sup>Department of Family Practice, The University of British Columbia, Vancouver, Canada; <sup>3</sup>Canadian Institutes of Health Research, Ottawa, Canada; <sup>4</sup>Canadian Institutes of Health Research-Institute of Musculoskeletal Health and Arthritis, Vancouver, Canada; <sup>5</sup>Department of Physical Therapy, The University of British Columbia, Vancouver, Canada, clare.ardern@ ubc.ca; <sup>6</sup>Sport and Exercise Medicine Research Centre, La Trobe University, Melbourne, Australia. <h4>Conflict of Interest Disclosures</h4> Matthew Hogel is Deputy Director, Funding Analytics at the Canadian Institutes of Health Research (CIHR). Alannah Brown is Senior Advisor to the Associate Vice-President at CIHR. Adrian Mota is Acting Vice President, Research—Programs at CIHR. Karim M. Khan is Scientific Director for CIHR’s Institute of Musculoskeletal Health and Arthritis (20172025). No other conflicts were reported. <h4>Funding/Support</h4> This work was supported by a CIHR Research Operating Grant (Scientific Directors) held by Karim M. Khan. CIHR’s Funding Analytics coordinated data management and analysis as part of its mandate to foster and deliver high quality peer review for health research in Canada. <h4>Role of the Funder/Sponsor</h4> CIHR did not participate in preparing the abstract, nor in the decision to submit the abstract for presentation.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,041
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesÉtudes des sciences et des technologies
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,953
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0410,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0040,010
Études des sciences et des technologies0,0030,010
Communication savante0,0000,000
Science ouverte0,0040,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,218
Tête enseignante GPT0,511
Écart entre enseignants0,293 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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