Canadian Adaptation of REVEAL(OT) for Chronic Pain Management: Rapid Qualitative Analysis Results
Bibliographic record
Abstract
Background. Chronic pain brings on many lifestyle changes. Redesign your Everyday Activities and Lifestyle with Occupational Therapy (REVEAL(OT)) is an evidence-based Danish intervention that benefits the daily functioning of individuals living with chronic pain. Purpose. To understand the perspectives of patients, clinicians and managers about REVEAL(OT) and identify format- and content-related modifications that are necessary to adapt to the Canadian context. Method. Based on a qualitative descriptive approach, focus groups and individual interviews with partners ( n = 45 participants in total) were conducted in two Montreal specialized pain clinics. The interview guide was inspired by Proctor's implementation model and a qualitative rapid analysis was performed. Findings. Participants recognized the need for (a) a flexible, personalized and hands-on intervention, (b) integrating multimodal approaches, (c) support to develop and implement healthy life habits, (d) addressing gaps in care supporting the need for an OT intervention, and (e) pragmatic organisational considerations for implementation. Conclusion. REVEAL(OT) is capable of addressing the occupational needs of patients but adaptations are required to fit within a new healthcare ecosystem. Our findings promote the use of lifestyle-oriented interventions along with current care and will generate the initial intervention manual to be tested and refined.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".