Does feeling pain help prepare future clinicians to treat pain? A qualitative exploration of a novel experiential approach to teaching health professional students about pain
Bibliographic record
Abstract
BACKGROUND AND INTRODUCTION: Pain is a subjective phenomenon, that is often misunderstood and invalidated. Despite recent advances in health professional training, it remains unclear how students should be taught about the subjectivity of pain. This study explored how a novel teaching activity that integrated physiotherapy students' first-hand experiences with laboratory-induced pain could address this gap. OBJECTIVE: The study aimed to explore the experiences and perceptions of physiotherapy students in relation to a workshop where physiotherapy students experienced laboratory induced pain. METHODS: We used a descriptive qualitative research design. Eighteen students participated in interviews. Transcripts were analyzed using an inductive conventional content analysis. RESULTS: We identified four overarching themes: 1) First-hand pain experiences facilitated engagement in learning; 2) Reflecting on these personal and peer pain experiences helped students make sense of pain; 3) The learning activity helped students understand the inherent subjectivity and complexity of pain; and 4) Students saw benefits for clinical practice. CONCLUSION: Integrating first-hand pain experiences within entry-level physiotherapy training appears to have novel value in helping students understand pain subjectivity. Future research should use robust and controlled designs to explore how this novel approach can be used to facilitate further understanding and empathy within clinical interactions with people living with 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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".