The experience of people with multiple sclerosis who receive occupational performance coaching
Bibliographic record
Abstract
Objective: To explore how Occupational Performance Coaching (OPC) influences self-management in the daily lives of people with Multiple Sclerosis (PwMS). Methods: A qualitative study involving 10 PwMS who underwent 6 sessions of telephone OPC over ten weeks. Interpretive description was used as the methodological approach. Participants were interviewed pre- and post-intervention, with thematic analysis performed on transcripts. Results: Pre-intervention themes included resisting MS, living with MS, ongoing challenges, and strategies. Post-intervention, the theme of resisting MS dissipated, with emergent sub-themes of planning ahead, being consistent, and talking about the plan. Participants reported reduced resistance towards their condition, a shift in their focus from problems towards solutions, and an enhancement of existing strategies and/or development of new strategies used to overcome ongoing challenges in living with MS. Conclusion: OPC may facilitate a shift in focus towards solutions and enhance self-management strategies in PwMS. Innovation: This study highlights OPC as a promising and innovative approach for addressing the self-management needs of individuals with MS, emphasizing its potential to enhance meaningful participation by fostering effective coping strategies and proactive attitudes.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".