The QUIPPED Project: Students’ Attitudes Toward Integrating Interprofessional Education into the Curriculum
Bibliographic record
Abstract
Background: Healthcare learners are the future healthcare providers. The preparationthey receive in their pre-registration programs will be vital to ensuring that they practice patient-centred care. One approach to this learning model is interprofessional education (IPE).Methods and Findings: Learner attitudes towards interprofessional education and practice were obtained over a thirty-three month period from the Queen’s University Inter-Professional Patient-centred Education Direction (QUIPPED) project. Attitudes were measured by questionnaires based on a 6-point Likert scale. The learners received a number of opportunities to engage in IPE and demonstrated positive attitudes. The degree to which one or more educational initiatives can make a difference in attitude is limited. However, over the course of the project both statistically significant and clinically meaningful differences were found. Different health professional student groups, including medical, medical radiation technology, nursing, occupational therapy, and physical therapy learners varied in their attitudinal responses, demonstrating they have already acquired professional identity.Conclusions: Recommendations include offering varied opportunities to learners, and providing them with the tools to communicate and collaborate together. Limitations include a lack of empirical evidence as to whether IPE translates into interprofessional practice and better patient-centred care.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".