Toward a New Model of Training in Canadian Forensic Psychiatry.
Bibliographic record
Abstract
The Royal College of Physicians and Surgeons of Canada has recently introduced a new model of training for residents and fellows in all specialties and subspecialties, including forensic psychiatry. This model, Competence by Design, is intended to improve the training of residents with the goal of increasing the competence of practicing specialists. In the Competence by Design model, training is broken down into four distinct phases. Residents prompt their supervisor proactively when they are ready to be assessed on a specific task, and the feedback is specific and corrective. A resident's performance of each designated task is reviewed by a competency committee, which decides when the trainee is ready to move on to the next phase. In this article, we review some of the problems with the current model of training and explore how this new model will improve upon this training. We anticipate that this model will prove effective at improving training in forensic psychiatry.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".