Evaluation of an Interprofessional Problem-based Learning Module on Care of Persons Living with HIV/AIDS
Bibliographic record
Abstract
Background: Interprofessional education (IPE) holds great promise in continuing to reform the management of complex chronic conditions such as HIV/AIDS, and Problem-based Learning (PBL) is a suitable format for IPE. This study aimed to evaluate the effectiveness of a large scale, compulsory interprofessional PBL module on HIV/AIDS education. In 2004, 30 physical therapy and 30 medical students at the University of Saskatchewan engaged in the HIV/AIDS PBL module. By 2007 over 300 students from seven healthcare programs were involved.Methods and Findings: The module was evaluated over the years using student satisfaction surveys, focus groups, self-assessments, and in 2007 with written pretest/post-tests. Students rated the learning experience about both HIV/AIDS and about interprofessional collaboration, at 4 or 5 out of 6 and effect sizes fell between d = .70 and 3.19. That only one pre-test/post-test study was conducted at a single institution is one of the limitations of this study.Conclusions: Students generally thought highly of the interprofessional PBL module on HIV/AIDS and learned a considerable amount. Although more research is needed to substantiate the self-assessment data, establish what and how much is being learned, and compare PBL to alternative methodologies, PBL is a promising approach to IPE.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".