The experience of occupational therapists and physiotherapists using a prototype, evidence-informed online knowledge translation resource to learn about patient-defined, personally-meaningful chronic pain rehabilitation
Bibliographic record
Abstract
Purpose: Chronic pain is a complex biopsychosocial experience, and rehabilitation helps people to manage pain, and restore valued life roles. Evidence suggests that more positive outcomes occur when clients perceive their rehabilitation to be meaningful. People with chronic pain describe rehabilitation as personally-meaningful when they develop a genuine connection with a credible therapist who they see as a guiding partner, and when rehabilitation holds personal value, is self-defined, and relevant to their sense of self-identity. This paper presents a qualitative study of therapists' experience using an e-learning package on patient-defined, personally-meaningful rehabilitation. Methods: A qualitative descriptive design was used to explore rehabilitation therapists' experience of a prototype evidence-informed, online resource developed on the basis of eLearning and web-design principles. Semi-structured interviews and focus groups were conducted with a purposive sample of occupational therapists and physiotherapists, and inductive coding and thematic analysis of transcripts was completed. Findings: Twenty-four therapists (12 occupational therapists, 12 physiotherapists) participated, representing a mix of gender and experience (early career; experienced; and specialist). Four themes and 12 sub-themes emerged from the analysis. The resource delivered a positive user experience, which added (translational) value to enhance learning, and participants were highly positive about the future potential of the resource to translate chronic pain rehabilitation research for early career, experienced, and specialist rehabilitation therapists. Conclusion: Results suggest that the disparate learning needs of rehabilitation therapists from diverse professional backgrounds and experience, may be addressed through the one resource. Participant feedback provides evidence that the resource fits with current models of learning and behaviour change. This study demonstrates the importance of basing online resources on eLearning and web-design principles to translate complex biopsychosocial chronic pain rehabilitation research for rehabilitation therapists.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 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".