Cross-sectional-derived determinants of satisfaction with physician-scientist training among Canadian MD/PhD graduates
Bibliographic record
Abstract
Although MD/PhD programs require considerable commitment on behalf of students and learning institutions, they serve as an integral means of training future physician-scientists; individuals who engage in translational medicine. As attrition from these programs has longstanding effects on the community of translational medicine and comes at substantial cost to MD/PhD programs, we aimed to identify determinants that were associated with satisfaction among MD/PhD graduates, a feature that might inform on limiting program attrition. Anonymized data from a national survey of 139 Canadian MD/PhD alumni was analyzed. Factor analysis was conducted to evaluate the reliability of three questions that measured satisfaction and logistic regression was used to assess the association of outcomes with 17 independent determinants. Eighty-one percent of graduates were satisfied with MD/PhD training. Factor analysis confirmed the reliability of the questions measuring satisfaction. Determinants of self-reported satisfaction with physician-scientist training included co-authorship of more than six manuscripts during MD/PhD training. Additionally, protected research time at the place of current appointment was strongly associated with agreement that MD/PhD training had helped career progression. Demographic variables were not associated with any satisfaction indicator. Taken together, the majority of Canadian MD/PhD graduates are satisfied with their physician-scientist training. Project collaboration leading to co-authorships and protected research time were strongly associated with training satisfaction among graduates. If the value of collaboration can be realized among current and future physician-scientist trainees who are dissatisfied with their training, this might ultimately reduce program attrition.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".