Empowering people living with chronic pain to use self-management strategies in their daily lives: understanding occupational therapy practices
Bibliographic record
Abstract
PURPOSE: Chronic pain affects daily functioning and quality of life for many individuals. Occupational therapists significantly contribute by facilitating self-management strategies, yet specific processes used remain under-documented. This study describes occupational therapy processes supporting chronic pain self-management. METHODS: A descriptive qualitative design involving three focus groups with 15 occupational therapists experienced in chronic pain management was used. Thematic analysis was used to identify key competencies and processes. RESULTS: Three main competencies were targeted in occupational therapy interventions: understanding one's condition, effectively using chronic pain self-management strategies, and self-regulating to engage in meaningful activities. To develop these competencies, occupational therapists implemented multiple key processes, including establishing a strong therapeutic bond, supporting individuals in identifying meaningful goals, understanding and tailoring interventions to pain-related occupational patterns, providing experimentation opportunities to foster self-management strategy development, facilitating the acquisition of self-regulation skills, and developing a personalized toolbox and relapse self-management plan. CONCLUSION: Occupational therapists foster chronic pain self-management through structured interventions, experiential learning, and transformative processes, enabling adaptive strategy integration into daily routines. This promotes competency development, enhancing daily functioning and meaningful engagement as well as highlighting occupational therapy's role in improving quality of life for individuals with chronic pain.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".