Predictors of sustained research involvement among MD/PhD programme graduates
Bibliographic record
Abstract
CONTEXT: MD/PhD programmes provide structured paths for physician-scientist training. However, considerable proportions of graduates of these programmes do not pursue careers in research consistent with their training. OBJECTIVES: We sought to identify factors associated with sustained involvement in research after completion of all postgraduate training. METHODS: Anonymised data from a national survey of Canadian MD/PhD programme graduates who had completed all physician-scientist training (n = 70) were analysed. Multivariable logistic regression was used to measure the associations between characteristics of graduates and five indicators of sustained research involvement following postgraduate training: (i) protected research time in the current appointment; (ii) percentage of time dedicated to research; (iii) planned future involvement in research; (iv) role as a principal investigator on a recent funded project, and (v) receipt of funding from a federal granting agency since graduation. RESULTS: The majority of graduates were significantly involved in research on the basis of at least one outcome. Completion of a research fellowship, number of first-authored or co-authored manuscripts published during MD/PhD training, and duration of MD/PhD training were positively associated with continued research involvement. Completion of a Masters degree prior to MD/PhD training, female gender, debt greater than CAD$50 000 at completion of training, and pursuit of a clinical specialty other than internal medicine, paediatrics, neurology, pathology and the surgical specialties were negatively associated with sustained research involvement. CONCLUSIONS: Most MD/PhD programme graduates remain significantly involved in research, but this involvement often does not correspond to traditional physician-scientist roles, in which a majority of time is dedicated to research. To minimise loss of investment in physician-scientist training, MD/PhD programmes should prioritise research productivity during training and the pursuit of additional research training during residency, and policymakers should establish stable sources of funding to reduce debt among graduates. Our data suggest further study is warranted to identify interventions to reduce attrition among female MD/PhD programme graduates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.164 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".