Mentorship needs and satisfaction of MD-PhD and MD-MSc trainees with marginalized identities: the Canadian experience
Bibliographic record
Abstract
Physician-scientists (PS) are the backbone of translational research and groundbreaking medical developments in many developed countries. Formalized PS training programs in the form of MD-PhD/MD-MSc programs in Canada have had a longstanding tradition of producing many exceptional PS researchers, staff, and faculty. However, despite these programs’ growth in size and popularity, PS trainees have consistently reported high rates of burnout, attrition, and lack of adequate mentorship and financial support. Additionally, there has been no formalized research examining the needs and prevalence of PS trainees who identify as having a marginalized identity, who may present different unaddressed needs. For the first time, this work captures the demographics, satisfaction, and needs of Canadian MD-PhD/MD-MSc trainees at the intersection of multiple marginalized identities. The authors utilized data from a cross-sectional survey conducted between June to December 2021 by the Clinician Investigator Trainee Association of Canada. Authors examined five marginalized identities covered by the survey: identifying as a woman, living with a disability, a visible minority, not being born in Canada, and being a primary caregiver during training. Results show that 78% of PS trainees self-identify with a marginalized identity and 47% with at least two identities, a proportion higher than previously published. In addition, specific marginalized identity groups reported differing needs as top priority. Moreover, PS trainees who identified with having a disability and who were primary caregivers during training reported significant dissatisfaction within their training programs. In addition to unique needs for each identity group, certain needs were expressed by all marginalized identity groups such as lack of adequate and specific mentorship during training. This research contributes novel data by examining an underrepresented group of PS trainees and supports previous calls to action within the literature expressing the need for greater oversight and infrastructure to support PS training in Canada.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".