Australian Clinician’s Views on Interprofessional Education for Students in the Rural Clinical Setting
Bibliographic record
Abstract
AbstractBackground: Collaboration between education providers and clinical agencies to develop models that facilitate cross-disciplinary clinical education for students is essential to produce work-ready graduates.Methods and Findings: This exploratory study investigated the perceptions of and opportunities for interprofessional education (IPE) from the perspectives of 57 clinical staff from three regional/rural health services across Victoria, Australia. Data were collected through a semi-structured questionnaire, interviews, and focus group discussions with staff from 15 disciplinary groups who were responsible for clinical education. Although different views emerged on what IPE entailed, it was perceived by most clinicians to be valuable for students in enhancing teamwork, improving the understanding of roles and functions of team members, and facilitating common goals for patient care. While benefits of IPE could be articulated by clinicians, student engagement with IPE in clinical areas appeared to be limited, largely ad hoc, and opportunistic. Barriers to IPE included: timing of students’ placements, planning and coordination of activities, resource availability, and current regulatory and education provider requirements.Conclusions: Without the necessary resources and careful planning and coordination, the integration of IPE as a part of students’ clinical placement experience will remain a largely untapped resource.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".