Developing consensus among people living with pain on what health professionals should know about their lived experiences
Bibliographic record
Abstract
PURPOSE: To address suboptimal pain competency in undergraduate health professional education programs, several organizations are now including people with chronic pain in courses to teach students about the experience of living with pain. This study aimed to achieve consensus among people living with pain regarding what health professions students should learn about the experience of pain. MATERIALS AND METHODS: A participatory three-round Delphi process was used, where 27 people living with pain voted on sub-themes about the experience of pain. Sub-themes achieving at least 75% agreement were approved, while others were revised and presented again in subsequent rounds. Inductive content analysis of the approved sub-themes was conducted, and participants endorsed the final product. RESULTS: A total of 39 sub-themes were approved, grouped into seven higher-order themes, including: the personal impacts of pain; harmful effects of stigma; adapting to living with pain; managing pain; connections between pain and mental health; challenges in pain assessment and diagnosis; and expectations from healthcare. CONCLUSIONS: The consensus themes are considered relevant across various health professions and can serve as a valuable aid for pain educators to assess their teaching and guide the involvement of people living with pain in educational activities.
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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 | 0.098 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| 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".