Development of a pain management competency assessment for physiotherapy students: Integrating simulation and written assessments
Bibliographic record
Abstract
Introduction Chronic pain is a global challenge resulting in substantial healthcare costs. Despite its prevalence, gaps in pain management education persist across health professions education programs. Developing an assessment to evaluate student competency in pain management is essential to identify and address the potential impact of these disparities on learning outcomes. This study describes the development and initial evaluation of the Pain Education in Physiotherapy (PEP) competency assessment, aimed at assessing student level of competency in pain management across entrylevel physiotherapy (PT) programs.Methods The assessment was developed using the DeVellis process, incorporating integrated knowledge translation principles and ongoing partner engagement. A steering group guided the creation of case-based multiple choice questions (MCQs) and simulation-based stations to assess competencies for pain management at different levels of Miller’s Pyramid. Initial evidence supporting the validity argument was gathered from PT students in their final semester of education (n = 146 for MCQs; n = 53 for simulations).Results Twenty-eight MCQ items and three simulation-based stations were selected. The MCQ component showed moderate internal consistency (α = 0.65), and the simulation-based assessments demonstrated moderate internal consistency (α = 0.63) with good interrater reliability (ICC2,1 range: 0.73–0.86).Discussion The PEP assessment incorporates case-based MCQs and simulation-based assessment stations to address critical interpersonal skills such as communication and empathy, often overlooked in traditional written assessments. This approach fills gaps in pain management education and provides a more comprehensive assessment tailored to PT needs.Conclusion This assessment represents an important advancement in the assessment of pain management competencies. Its rigorous development process, partner engagement, and promising initial evaluation underscore its potential to identify gaps in pain education and help improve outcomes related to PT education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".