Research-Track Programs for Residents in Psychiatry: A Review of Literature and a Report of 3 Canadian Experiences
Bibliographic record
Abstract
OBJECTIVES: Clinician-scientists occupy an interesting position at the interface between science and care, and have a role to play in bridging the 2 valleys between fundamental and clinical research, and between clinical research and clinical practice. However, research training during medical residency for future clinician scientists is an important but challenging process. Our article, written by residents and directors of research-track (RT) programs, aimed at reviewing literature on RT programs for residents, and describing the organization of RT programs at 3 Canadian universities (the University of British Columbia, the University of Toronto, and McGill University). METHODS: A systematic MEDLINE search was conducted for the review section. Psychiatry program directors in Canada were also contacted to provide information about potential RT programs. RESULTS: Twenty articles were related to resident RT programs in medicine, including 6 in psychiatry. Moreover, 5 out of 16 Canadian programs were found to offer a formal RT program, of which 3 are described here. Most reviewed articles described the program organization, while only one provided an outcome assessment with evidence of increased scholarly activity following RT implementation. CONCLUSIONS: Our article sheds light on postgraduate programs aiming at facilitating the dual training of future clinician-scientists, and developed during the last 10 years. It also highlights the lack of outcome assessment, and the paucity of guidelines to organize these programs in relation to the national requirements.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.036 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".