The Use of a Modified Delphi Technique to Inform the Development of Best Practice in Interprofessional Training for Collaborative Primary Healthcare
Bibliographic record
Abstract
Background: Primary healthcare (PHC) education and training is directed to a diverse range of health professionals at undergraduate, postgraduate, and professional levels. Increasing emphasis is being placed on PHC professionals working together in delivering better care and improving patient outcomes. This article reports on using a modified Delphi technique to determine the level of consensus on a series of statements across four domains of interprofessional education (IPE) for collaborative practice: big picture, organization, capabilities, teaching, and learning. Methods and Findings: The modified Delphi technique used three Delphi rounds: the first round comprising workshops, interviews, or online survey; the remaining rounds used online surveys. A panel of 56 PHC medical, nursing, allied health, and workforce experts participated. There was consensus on a set of capabilities for interprofessional learning outcomes and on a range of teaching and learning strategies. Areas for further consideration included identifying interprofessional training opportunities through continuing professional development, and tailoring team-based approaches to diverse PHC settings. Conclusion: The modified Delphi technique used in this project demonstrated a successful engagement of a heterogeneous panel of PHC experts. The principles of IPE for collaborative practice and strategies for delivering interprofessional training could apply across various PHC settings.
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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 | 0.229 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".