Healthcare workers’ understanding of interprofessional education and collaborative practice in regional health settings: A survey study
Bibliographic record
Abstract
Introduction: Interprofessional education and collaborative practice can enhance outcomes for patients and families. Facilitating collaborative practice at the point of care requires skilled healthcare workers who understand relevant terminology and concepts. However, gaps persist in the understanding and facilitation of interprofessional education and collaborative practice in some health settings. This survey study investigated healthcare workers’ perceived knowledge and understanding of interprofessional education and collaborative practice. Methods: A bespoke online survey was administered to healthcare workers from two regional health services in Queensland, Australia. Data were subject to descriptive and inferential analyses. Results: Data were available from 235 healthcare workers. Multiple regression analysis revealed that claiming to understand the difference between different models of service delivery, the ability to explain interprofessional education to a colleague and being an allied health practitioner were statistically significant predictors of a high knowledge of interprofessional practice score. Very few respondents were trained in this area (6%, n = 14), and those trained reported higher confidence in facilitating interprofessional education and collaborative practice at the point of care. Conclusion: This study has highlighted the gaps in healthcare workers’ perceived knowledge and understanding of interprofessional education and collaborative practice. Healthcare workers need targeted professional development opportunities to develop the skills, knowledge and attitudes necessary for practising in interprofessional teams. They also need opportunities to facilitate it with students and others. Improving interprofessional and collaborative practice within teams will ultimately improve healthcare outcomes of patients serviced by healthcare workers.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".