Greats: Learning Strategies of Master Forensic Psychiatrists
Bibliographic record
Abstract
For forensic psychiatry to thrive as a profession, practitioners need to be committed to intentional, continuous learning and development throughout their careers. However, carving their way through the challenges of practice and finding room to grow can be daunting. Research can help lessen this burden by examining the careers of experienced and skilled practitioners, identifying the factors that influenced their development, and the strategies they used to direct it. To date, little research of this kind has been conducted in forensic psychiatry. In this study, we used the deliberate practice model of elite performance as a heuristic to interpret the accounts of several experienced and distinguished practitioners, revealing and characterizing the influences and activities they identify as having been most important to their development. Semi-structured telephone interviews were conducted with six participants from across North America who started their forensic careers between 1965 and 1980. Transcripts were analyzed using directed content analysis. Participants cited little in the way of highly structured activities designed specifically to improve performance. They instead described using opportunities to learn from real casework and additional knowledge pursuits, as well as using deliberate career management to structure the conditions of their work-based learning. They also stressed the effect of entering forensic practice during a period of increasing interest, demand and investment, which yielded early opportunities to learn through practice. We discuss limitations in the deliberate practice model’s capacity to capture key learning strategies in forensic psychiatry, connections between work-based learning and the discipline’s general historical trajectory, and the role of career management in professional development strategies.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".