Training Programs for Fundamental and Clinician-Scientists: Balanced Outcomes for Graduates by Gender
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".