Training Programs for Fundamental and Clinician-Scientists: Balanced Outcomes for Graduates by Gender
Notice bibliographique
Résumé
Background: Women scientists are less likely to obtain Assistant Professorship and achieve promotion, and obtain less grant funding than men. Scientist/clinician-scientist training programs which provide salary awards as well as training and mentorship are a potential intervention to improve outcomes among women scientists. We hypothesized whether a programmatic approach to scientist/clinician-scientist training is associated with improved outcomes for women scientists in Canada when compared with salary awards alone. Trainees within the Kidney Research Scientist Core Education and National Training Program (KRESCENT), Canadian Child Health Clinician Scientist Program (CCHCSP), and the Canadian Institutes of Health Research (CIHR) salary award programs were evaluated. Objective: To examine whether the structured KRESCENT training program with salary support improves academic success for women scientists relative to salary awards alone. Design: Retrospective cohort study. Setting: Canadian national research scientist and clinician-scientist training programs and salary awards. Participants: KRESCENT cohort (n = 59, 2005-2017), CCHCSP cohort (n = 58, 2002-2015), and CIHR (n = 571, 2005-2015) Salary Awardees for postdoctoral fellows (PDF) and new investigators (NI). Measurements: National operating grant funding success, achieving an academic position as an Assistant Professor for PDF, or achieving promotion to Associate Professor for NI. Methods: The gender distribution of each cohort was determined using first name and NamepediA and was examined for PDF and NI, followed by a description of trainee outcomes by gender and training level. Results: KRESCENT and CIHR PDF were balanced (12/27, 44% men and 55/116, 47% women) while CCHCSP had a higher proportion of women (13/20, 65%). KRESCENT and CCHCSP NI retained women scientists (19/32, 59% and 22/38, 58% women), whereas CIHR NI had fewer women (165/455, 36% women vs 290/455, 64% men, P = 0.01). There was a high rate of NI operating grant success (91%-95%) with no gender differences in each cohort. There was a high proportion of CCHCSP PDF who achieved an Assistant Professorship (18/20, 90%) that may be due in part to a longer follow-up period (9.3 ± 3 years) compared with KRESCENT PDF (7/27, 26%, 0.88 ± 4.5 years), and these data were not available for CIHR PDF. Women KRESCENT NI showed increased promotion to Associate Professor ( P = 0.02, 0.25 ± 3.2 years follow-up) and CCHCSP NI had high promotion rates (37/38, 97%, 6.9 ± 3.6 years follow-up) irrespective of gender. There was an overall trend toward more men pursuing biomedical research. Limitations: KRESCENT and CCHCSP training program cohort size and heterogeneity; assigning gender by first name may result in misclassification; lack of data on the respective applicant pools; and inability to examine intersectionality with gender, ethnicity, and sexual orientation. Conclusion: Overall trainee performance across programs is remarkable by community standards regardless of gender. KRESCENT and CCHCSP training programs demonstrated balanced success in their PDF and NI, whereas the CIHR awardees had reduced representation of women scientists from PDF to NI. This exploratory study highlights the utility of programmatic training approaches like the KRESCENT program as potential tools to support and retain women scientists in the academic pipeline during the challenging PDF to NI transition period.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».