Forensic Occupational Therapy in Canada: The Current State of Practice
Bibliographic record
Abstract
Although occupational therapists have been practicing in forensic settings for many years, there is a paucity of literature regarding the nature of this practice in Canada. The purpose of this study was to describe the practices of Canadian occupational therapists in forensic mental health. An online survey was designed based on the Canadian Practice Process Framework. Following purposive and snowball sampling, responses were analysed with descriptive statistics and content analysis. Twenty-seven clinicians responded (56% response rate). Respondents indicated commonalities in workplaces, client caseloads and practice challenges. The outstanding need in Canada to demonstrate client outcomes through the use of evaluation instruments reflects those practice gaps identified internationally. Education, advocacy and research are critical areas for the development of Canadian forensic occupational therapy. Although findings heavily reflect one provincial context and may not be generalizable to nonhospital settings, a number of priority areas were identified. Future efforts should clarify the role of forensic occupational therapy to stakeholders, and validate their contributions through research that evaluates intervention efficacy and meaningful outcomes. Copyright © 2016 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".