FEATURE ARTICLE. Choosing Child and Adolescent Psychiatry: Factors Influencing Medical Students
Bibliographic record
Abstract
Volpe et al Objective: To examine the factors influencing medical students to choose child and adolescent psychiatry as a career specialty. Method: Quantitative and qualitative methods were used. A web-based survey was distributed to child and adolescent psychiatrists at the University of Toronto. In-depth interviews were held with select child and adolescent psychiatrists as well as a focus group with psychiatry residents. Retrospective accounts of the factors that influenced their decision to choose psychiatry and/or child and adolescent psychiatry as a specialty were collected. Results: Ninety-two percent of participants indicated that recruitment of child psychiatrists in Canada is a problem. The recent decision by the Royal College of Physicians and Surgeons to recognize child and adolescent psychiatry as a subspecialty and introduce an extra year of training was identified as a further challenge to recruitment efforts. Other deterrents included lower salary than other subspecialties, lack of exposure during training, stigma, and lack of interest in treating children. Recruitment into psychiatry was enhanced by good role modeling, early exposure in medical school, an interest in brain research, and career and lifestyle issues. Conclusions: A rebranding of the role and perception of psychiatry is needed to attract future psychiatrists. Early exposure to innovations in child and adolescent psychiatry and positive role models are critical in attracting medical students. Recruitment should begin in the first year of medical school and include an enriched paediatric
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".