Promoting Interprofessional Education and Collaborative Practice in Rural Health Settings: Learnings from a State-Wide Multi-Methods Study
Bibliographic record
Abstract
Evidence is mounting regarding the positive effects of Interprofessional Education and Collaborative Practice (IPECP) on healthcare outcomes. Despite this, IPECP is only in its infancy in several Australian rural healthcare settings. Whilst some rural healthcare teams have successfully adopted an interprofessional model of service delivery, information is scarce on the factors that have enabled or hindered such a transition. Using a combination of team surveys and individual semi-structured team member interviews, data were collected on the enablers of and barriers to IPECP implementation in rural health settings in one Australian state. Using thematic analysis, three themes were developed from the interview data: IPECP remains a black box; drivers at the system level; and the power of an individual to make or break IPECP. Several recommendations have been provided to inform teams transitioning from multi-disciplinary to interprofessional models of service delivery.
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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 | 0.084 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".