Performance report for a 10-year-old MD/PhD Program: A survey of trainees at the University of Ottawa
Bibliographic record
Abstract
PURPOSE: Integrated MD/PhD programs are relatively new in Canada and represent a platform to train the next generation of clinician-scientists. However, MD/PhD programs vary substantially by structure, funding and mentorship opportunities, and there exists a paucity of data on the overall students' successes and challenges. The purpose of this study is to assess objective and subjective metrics of the MD/PhD Program at the University of Ottawa. METHODS: Students in all years of the program were invited to complete a 58- question survey, and the resulting data were analyzed by descriptive statistics. RESULTS: Our survey had an 88.5% (23/26) participation rate. The program has been gaining interest and the number of applications increased by 178% between 2013 and 2018. Tuition support was considered an essential element in accepting the admission offer, as 47.8% of students would have declined admission without full tuition coverage. The MD/PhD students were heavily engaged in scholarly activities, with an average of 8.3 presentations/ publications per respondent. Respondents indicated low satisfaction with formal career planning advice (28.6% satisfied/very satisfied) and program transition guidance (22.2%). When delivered informally by peers, both career planning advice and program transition guidance were experienced as more satisfying (65.2% and 63.6%, respectively). Only 34.8% of survey respondents identified as female, highlighting the challenge of achieving diversity in clinician-scientist training programs. CONCLUSION: Our report contributes to the body of knowledge on concrete obstacles experienced by students within MD/PhD programs and key areas that can be improved upon-locally, provincially and nationally-to further advance student success.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".