Evaluating an Innovative eLearning Pain Education Interprofessional Resource: A Pre–Post Study
Bibliographic record
Abstract
Objective: The challenges of moving the pain education agenda forward are significant worldwide, and resources, including online, are needed to help educators in curriculum development. Online resources are available but with insufficient evaluation in the context of prelicensure pain education. Therefore, this pre-post study examined the impact of an innovative eLearning model: the Pain Education Interprofessional Resource (PEIR) on usability, pain knowledge, beliefs, and understanding of pain assessment skills including empathy. Methods: Participants were students (N = 96) recruited from seven prelicensure health sciences programs at the University of Toronto. They worked through three multifaceted modules, developed by an interprofessional team, that followed a patient with acute to persistent postsurgical pain up to one year. Module objectives, content, and assessment were based on International Association for the Study of Pain Pain Curricula domains and related pain core competencies. Multimedia interactive components focused on pain mechanisms and key pain care issues. Outcome measures included previously validated tools; data were analyzed in SPSS. Online exercises provided concurrent individual feedback throughout all modules. Results: The completion rate for modules and online assessments was 100%. Overall usability scores (SD) were strong 4.27/5 (0.56). On average, pain knowledge scores increased 20% (P < 0.001). The Pain Assessment Skills Tool was sensitive to differences in student and expert pain assessment evaluation ratings and was useful as a tool to deliver formative feedback while engaged in interactive eLearning about pain assessment. Conclusions: PEIR is an effective eLearning program with high student ratings for educational design and usability that significantly improved pain knowledge and understanding of collaborative care.
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 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 | 0.030 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".