The Risks and Benefits of Humour Use With Individuals After Stroke
Bibliographic record
Abstract
Background. Previous research addressing the use of humour in rehabilitation has demonstrated positive benefits that have potential to contribute to collaborative relationship-focused practice. Some research points toward the multidimensional functions of humour in stroke practice, although little is known about the use of humour within occupational therapy specifically. Purpose. This study aims to uncover Canadian occupational therapists’ perspectives on the risks and benefits of humour use with people who have sustained a stroke. Method. Through a Delphi study consisting of three rounds of surveys, occupational therapists identified and ranked risks and benefits of using humour. Findings. Consensus was reached on a total of 32 benefits, highlighting the role of humour in building rapport, enhancing communication, promoting emotional well-being, and increasing client motivation and engagement in therapy. These findings underscore the potential of humour to build relational capital and thereby potentially facilitate positive rehabilitation outcomes in ways consistent with collaborative relationship-focused practice. Two risks associated with humour use also gained consensus, focusing on comprehension/interpretation challenges and differences in humour preferences between clients and therapists. Conclusion. This study contributes evidence-informed insights to guide clinical practice and education, advancing our understanding of humour use as a relational practice of use in promoting collaborative relationship-focused practice for individuals post-stroke.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".