Learning and identity development during interprofessional hospital placements: A qualitative exploration using rich pictures
Bibliographic record
Abstract
CONTEXT: Increasing numbers of healthcare students are trained within interprofessional hospital placements, where they learn to be part of the landscape of healthcare practice. Explicitly facilitating (inter)professional identity development has been recommended as a goal of these placements. We aimed to explore students' experiences during their placement, and its relation to their learning and identity development. METHODS: In this multicenter qualitative study, nine medical students, six midwifery students, and six nursing students drew rich pictures of one satisfying and one challenging experience during their interprofessional placement, capturing complex, nonverbal elements of these experiences. We used semi-structured interviews to deepen understanding of students' experiences and their developing identities, adopting an inductive constructivist thematic analysis. RESULTS: During their interprofessional encounters, a range of emotions supported or challenged students' learning and identity development. These emotions played a pivotal role throughout the three themes we identified: (1) Understanding and appreciating differences; (2) Navigating identity tensions; and (3) Gaining confidence in patient-centered learning and collaboration. CONCLUSIONS: During interprofessional placements, most students engage in learning about each other's responsibilities and values, enhancing knowledgeability. Tutors should be aware of students' emotions during interprofessional encounters, and stimulate reflection on them, as emotions can foster or hamper students' knowledgeability and identity development.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 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".