Problem-based learning for inter-professional education: evidence from an inter-professional PBL module on palliative care
Bibliographic record
Abstract
INTRODUCTION: The objective of this article was to analyze the theory and pedagogical basis of the use of problem-based learning (PBL) for inter-professional education (IPE) in undergraduate health science education and present evidence from a palliative care iPBL (inter-professional PBL) module that confirms the importance of the two methodologies being used together. METHODS: More than 1000 student surveys collected over 4 years were analyzed for components of usefulness, enjoyment and facilitator effectiveness. A retrospective self-assessment of learning was used for both content knowledge of palliative care and knowledge of the other professions participating in the module. RESULTS: Statistically significant gains in knowledge were recorded in both areas assessed. Medical students reported lower gains in knowledge than those in other programs. On a scale of 0 to 6, mean scores were moderately high for usefulness (4.37) and facilitator effectiveness (5.19). Mean scores for enjoyment of the iPBL module were very high at 5.25. CONCLUSION: There is strong theoretical and empirical evidence that PBL is a useful method to deliver IPE for palliative care education. With the evidence presented from the palliative care iPBL it is our contention that PBL inter-professional cases should be utilized more often, incorporated into IPE programs generally, and researched more rigorously.
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 | 0.009 | 0.067 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".