Making it happen: Development of an interprofessional deprescribing education programme
Bibliographic record
Abstract
Deprescribing is the planned and supervised reduction or discontinuation of medications that may be causing harm or are no longer benefiting a patient. The need for deprescribing to be a routine part of patient care is essential with an aging population and the rising prevalence of polypharmacy, which has been associated with increased adverse outcomes such as falls, hospitalizations and mortality. Deprescribing is a complex intervention that requires collaboration between the patient, caregivers and healthcare providers to adequately support all involved, as well as to ensure medications are not restarted in error. The objective of this article is to describe the stepwise approach to planning and ongoing development of an online, interprofessional deprescribing education programme for healthcare providers and students with the goal of enhancing deprescribing practice. There were four main planning and development components: (1) a needs assessment to provide guidance on programme design, development and delivery; (2) a consultative programme planning process with an advisory group of stakeholders and patient partners to inform programme learning outcomes and content; (3) a core development team for the creation of programme content; and (4) planning for programme evaluation. Based on the stepwise and consultative process, programme outcomes were identified, and five modules were developed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".