Ten-year mixed-method evaluation of prelicensure health professional student self-reported learning in an interfaculty pain curriculum
Bibliographic record
Abstract
Abstract See commentary: Trouvin A-P. “Ten-year mixed method evaluation of prelicensure health professional student self-reported learning in an interfaculty pain curriculum”: a view on pain education. PAIN Rep 2022;7:e1031. Introduction: Student perspectives on interprofessional pain education are lacking. Objectives: The purpose of this study was to evaluate ratings of knowledge acquisition and effective presentation methods for prelicensure health professional students attending the University of Toronto Centre for the Study of Pain Interfaculty Pain Curriculum (Canada). Methods: A 10-year (2009–2019) retrospective longitudinal mixed-methods approach comprising analysis and integration of quantitative and qualitative data sets was used to evaluate 5 core University of Toronto Centre for the Study of Pain Interfaculty Pain Curriculum learning sessions. Results: A total of 10, 693 students were enrolled (2009–2019) with a mean annual attendance of 972 students (±SD:102). The mean proportion of students rating “agree/strongly agree” for knowledge acquisition and effective presentation methods across sessions was 79.3% (±SD:3.4) and 76.7% (±SD:6.0), respectively. Knowledge acquisition or presentation effectiveness scores increased, respectively, over time for 4 core sessions: online self-study pain mechanisms module (P = 0.03/P < 0.001), online self-study opioids module (P = 0.04/P = 0.019), individually selected in-person topical pain sessions (P = 0.03/P < 0.001), and in-person patient or interprofessional panel session (P = 0.03). Qualitative data corroborated rating scores and expanded insight into student expectations for knowledge acquisition to inform real-world clinical practice and interprofessional collaboration; presentation effectiveness corresponded with smaller session size, individually selected sessions, case-based scenarios, embedded knowledge appraisal, and opportunities to meaningfully interact with presenters and peers. Conclusion: This study demonstrated positive and increasing prelicensure student ratings of knowledge acquisition and effective presentation methods across multifaceted learning sessions in an interfaculty pain curriculum. This study has implications for pain curriculum design aimed at promoting students' collaborative, patient-centered working skills.
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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 | 0.053 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".