Improving Understanding of Teaching Strategies Perceived by Interprofessional Learning (IPL) Lecturers to Enhance Students’ Formulation of Multidisciplinary Roles: An Exploratory Qualitative Study
Bibliographic record
Abstract
Background: Interprofessional Learning (IPL) is an educational process intended to equip health and social care students with appropriate knowledge, skills, and attitudes for effective interprofessional working. By and large, the literature review highlighted in this article has shown that IPL is a worthwhile pursuit, with some studies highlighting conflicts over best teaching methods to use. In response, the aim of this exploratory research was to improve understandings of teaching strategies perceived by IPL lecturers to enhance students' formulation of multidisciplinary roles.Methods: An exploratory qualitative study was carried out. Semi-structured interviews were conducted, with a purposive sample of 4 consenting IPL lecturers. The objectives of the study were to extend understandings of strategies believed to enhance or inhibit students' accurate assimilation of Allied Health Professional (AHP) roles, to nurture awareness of potential obstacles that may inhibit successful delivery of IPL, to promote insight into what constitutes quality delivery of IPL, and to identify potential topics for further research.Findings: Five themes emerged from the data: (1) IPL lecturers hold contrasting viewpoints about the need for IPL; (2) improved understanding of roles is directly proportional to time spent with AHPs; (3) perspectives differ about when and where IPL should be taught; (4) stereotyping and negative attitudes inhibit accurate role construction; and (5) positive role modelling by lecturers is important.Conclusions: This article acts in a conscience-raising manner and highlights five key areas of lecturers' understandings about how to effectively deliver IPL. This nurtured awareness will be used to develop and evaluate new implementations in IPL and education.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".