What Factors Contribute to Senior Psychiatry Residents' Interest in Geriatric Psychiatry? A Delphi Study
Bibliographic record
Abstract
OBJECTIVE: To generate hypotheses regarding factors that influence senior psychiatric residents, to consider treating geriatric patients in their future practices. METHOD: Using the Delphi technique, designed to generate ideas and consensus, we asked psychiatry residents at the University of Toronto who had completed, or were completing, their geriatric rotation about the factors they thought might influence residents in devoting some of their practice to geriatric patients. Residents then rated the degree of influence of these factors which had been synthesized into a questionnaire. RESULTS: Twenty-six items were rated according to their degree of influence. The most influential item was positive clinical experiences with seniors. This was followed closely by supervisor characteristics such as enthusiasm, role modeling, competence, and mentoring. Interest in and comfort with the medical psychiatric and neuropsychiatric nature of the field were also felt to be influential. CONCLUSIONS: The factors that influence senior psychiatry resident interest in the practice of geriatric psychiatry are primarily educational and result from exposure to the field under optimal educational circumstances (positive clinical experiences and excellent supervisors). The medical and neuropsychiatric nature of the field also likely exerts a unique influence and should be considered in stimulating interest in this population.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".