Bibliographic record
Abstract
As part of a rapidly spreading reform toward recovery-oriented services, mental health care systems are adopting Psychiatric/Psychosocial Rehabilitation (PSR). Accordingly, PSR education and training programs are now available and accessible. Although psychiatrists and sometimes other physicians (such as family physicians) provide important services to people with serious mental illnesses and may, therefore, need knowledge and skill in PSR, it seems that the medical profession has been slow to participate in PSR education. Based on our experience working in Canada as academic psychiatrists who are also Certified Psychiatric Rehabilitation Practitioners (CPRPs), we offer descriptions of several Canadian initiatives that involve physicians in PSR education. Multiple frameworks guide PSR education for physicians. First, guidance is provided by published PSR principles, such as the importance of self-determination (www.psrrpscanada.ca). Second, guidance is provided by adult education (andragogy) principles, emphasizing the importance of addressing attitudes in addition to knowledge and skills (Knowles, Holton, & Swanson, 2011). Third, guidance in Canada is provided by Canadian Medical Education Directives for Specialists (CanMEDS) principles, which delineate the multiple roles of physicians beyond that of medical expert (Frank, 2005) and have recently been adopted in Australia (Boyce, Spratt, Davies, & McEvoy, 2011).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.102 | 0.027 |
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".