Understanding students’ and clinicians’ experiences of informal interprofessional workplace learning: an Australian qualitative study
Bibliographic record
Abstract
OBJECTIVES: While postgraduate studies have begun to shed light on informal interprofessional workplace learning, studies with preregistration learners have typically focused on formal and structured work-based learning. The current study investigated preregistration students' informal interprofessional workplace learning by exploring students' and clinicians' experiences of interprofessional student-clinician (IPSC) interactions. DESIGN: A qualitative interview study using narrative techniques was conducted. SETTING: Student placements across multiple clinical sites in Victoria, Australia. PARTICIPANTS: Through maximum variation sampling, 61 participants (38 students and 23 clinicians) were recruited from six professions (medicine, midwifery, nursing, occupational therapy, paramedicine and physiotherapy). METHODS: We conducted 12 group and 10 individual semistructured interviews. Themes were identified through framework analysis, and the similarities and differences in subthemes by participant group were interrogated. RESULTS: Six themes relating to four research questions were identified: (1) conceptualisations of IPSC interactions; (2) context for interaction experiences; (3) the nature of interaction experiences; (4) factors contributing to positive or negative interactions; (5) positive or negative consequences of interactions and (6) suggested improvements for IPSC interactions. Seven noteworthy differences in subthemes between students and clinicians and across the professions were identified. CONCLUSIONS: Despite the results largely supporting previous postgraduate research, the findings illustrate greater breadth and depth of understandings, experiences and suggestions for preregistration education. Educators and students are encouraged to seek opportunities for informal interprofessional learning afforded by the workplace.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".