Education about deprescribing for pre‐licensed and licensed healthcare professionals: A scoping review
Bibliographic record
Abstract
Deprescribing is complex because it involves patients' health, values, and preferences. The World Health Organization and Canadian Medication Appropriateness and Deprescribing Network have recommended that deprescribing be integrated into health curricula, prompting the need for further understanding about deprescribing education. The purpose of this research is to describe the literature regarding deprescribing education provided to healthcare professionals. We conducted a scoping review using the five-step model by Arksey and O'Malley with revisions from Levac et al. The databases searched included Medline, Scopus, Embase and ERIC. Papers were included if they were written in English and contained an educational intervention about deprescribing tailored toward physicians, pharmacists or nurses. White papers and conference abstracts were included. A total of 4853 abstracts were eligible for screening and 46 papers were included (25 full texts, 15 conference abstracts and 6 white papers). Thirty-three papers utilized group education for their intervention and of these, 20 involved interactive portions. Medicine was the most targeted profession, included in 29 papers. The most common outcomes were the number of medications deprescribed and an increase in learner knowledge and self-efficacy regarding deprescribing using self-assessment surveys or post-educational examinations. We found that there is evidence that educational interventions can increase participant knowledge regarding deprescribing and improve self-efficacy. To expand the education of deprescribing, future interventions should engage and utilize a variety of health professions and interventions could include real patients. Further research is required to determine the retention and application of deprescribing knowledge gained from single educational interventions.
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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 | 0.015 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".