Learnings and challenges to deploy an interprofessional and independent medical education programme to a new audience
Bibliographic record
Abstract
The importance of interprofessional education (IPE) in continuing medical education and professional development has long been recognised by health organisations and academic societies, benefiting not only patient outcomes and interprofessional relationships but also overall health systems and workforce shortage. We report on the outcomes of an Australian IPE activity on medication-overuse headache (MOH) with general practitioners (GPs) and community pharmacists as learners. The design of the activity, which followed the predisposing-enabling-reinforcing instructional framework by Green and Kreuter, aimed to: (1) improve knowledge and foster a willingness in GPs and pharmacists to work collaboratively to enhance the prevention, diagnosis and management of MOH; and (2) address their educational gap by demonstrating the utility of a blended learning IPE strategy on MOH. Integrated into the activity was an assessment of its effectiveness and impact to instil change in the participants' knowledge of MOH, attitude and willingness to treat, and clinical practice behaviours of GPs and pharmacists to work together. The learners gained knowledge and confidence in diagnosing and managing MOH and in their ability to educate patients. The IPE approach suited the activity and was valued by the participating GPs and pharmacists, who seldom experience such learning formats. However, for educational providers in Australia, developing and deploying an independent medical education (IME) programme can be challenging. Providers of IMEs need to be aware of the potential pitfalls when competing with pharmaceutical-company-sponsored and delivered programmes.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".