Integrating people living with pain into pre-licensure pain education: a novel learning activity for health professional students
Bibliographic record
Abstract
PURPOSE: To explore the experiences of physiotherapy students and people living with pain regarding their participation in a novel partnered learning activity. MATERIALS AND METHODS: A concurrent mixed method design integrating quantitative and open-ended survey questions with focus group discussions was used. The activity included a one-on-one interaction between physiotherapy students and people living with chronic pain to explore the multidimensional impact of pain. Quantitative data were analyzed using descriptive statistics. Qualitative data were analyzed using a qualitative description approach. RESULTS: Twenty-five students and 42 people living with pain consented to participate. For students, three overarching themes were identified: (1) the activity was perceived as a transformative learning experience; (2) first-hand interactions, the learning tools, and the environment contributed to learning; and (3) some challenges fostered learning, while others impeded it. For people living with pain, three themes emerged: (1) translating lived experience into an empowering contribution; (2) the complex role of compensation; and (3) engagement driven by purpose and trust. CONCLUSION: The partnered learning activity served as a transformative learning experience for students and empowered people living with pain through meaningful contributions of their lived experience. Future research should explore the impact of partnered activities on student learning outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".