A Blended Active Learning Pilot: A Way to Deliver Interprofessional Pain Management Education
Bibliographic record
Abstract
This article presents an innovative approach to interprofessional education that places learning in the context of a specific clinical area that is relevant to pharmacy students as well as students from a number of other health professions; in this case pain management. Interprofessional pain education that teaches a team approach to pharmacy students is essential for improving pain management practices. The interprofessional education model presented, based on a pilot of a series of interprofessional pain management modules, is designed to be flexible, using a modular format that incorporates both online and face-to-face learning. The model was developed as a means of overcoming some of the challenges, such as scheduling, which make the integration of interprofessional education into curricula difficult. This technology enabled education model has been piloted and implemented with groups of pharmacy students who were placed into teams with students from other disciplines such as medicine, nursing, and social work. This article presents the educational strategy and its development; describes the interprofessional pain management modules; discusses findings from three pilot evaluations of the modules; shares lessons learned; and highlights the strengths of the approach.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".