Determining 24-Hour Supervision: A Scoping Review Through a Canadian Legal Database
Bibliographic record
Abstract
Background: A highly contentious and controversial aspect of assessments in a medico-legal private practice is the determination of whether or not a client or patient requires 24-hour supervision. Purpose: The intent of this article is to review legal cases involving Occupational Therapist’s (OTs) and the determination of 24-hour supervision in Canada. Methods: A search and scoping review of the Canadian Legal Information Institute (CANLII) was completed using the terms “OT” and “24-hour supervision”. A review of the literature and determination of critical observations were completed. Findings: Based on 46 legal proceedings since 1986, the following critical observations emerged: Lack of pattern in the areas assessed in determining 24-hour supervision, inconsistent quality of OT testimony related to determining 24-hour supervision, increased demand on OTs to determine if a client requires 24-hour supervision, and lack of guidelines for assessing 24-hour supervision. Implications: Without clear guidelines, OT testimony may bear less weight in court. Recommendations: The authors propose that an evidence-based framework upon which to formulate a determination of 24-hour supervision needs to be considered. Further research into current OT practice in determining 24-hour supervision and exploration of existing assessment tools for determining 24-hour supervision are recommended.
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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 | 0.066 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.096 | 0.098 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".