Recommending 24-hour attendant care: A qualitative study exploring the clinical decision-making process of occupational therapists in Ontario, Canada
Bibliographic record
Abstract
ObjectiveThis study aims to explore how occupational therapists working in private practices in Canada use clinical indicators and tools to determine if clients require 24-hour attendant care.DesignA qualitative research study.SettingThe setting involved semi-structured, one-on-one interviews with occupational therapists in Canada.ParticipantsOccupational therapists were selected through purposive sampling: (1) registered Canadian occupational therapists, (2) with over 10 years of private practice experience, and (3) who have assessed the need for 24-hour attendant care at least once before the study.Main measuresThe interviews were conducted, transcribed, coded, and thematically analyzed by two researchers using Braun and Clarke's protocol. The paper is also reported based on the consolidated criteria for reporting qualitative research guidance.ResultsThe study involved nine occupational therapists (eight women and one man), with 14 to 24 years of private practice experience in Ontario. Three main themes in the decision-making process for 24-hour attendant care were identified: (1) Individualized and Holistic Assessments; (2) Clinical Expertise-Based Decision-making; and (3) Risk Assessment in Decision-Making.ConclusionsThis study provides a greater understanding of the decision-making process of occupational therapists working in Canada when recommending 24-hour attendant care. However, further research and development of guidelines are needed to support occupational therapists in this area.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".