Supporting Interprofessional Collaboration in Deprescribing: Needs Assessment for an Education Program
Bibliographic record
Abstract
INTRODUCTION: : Deprescribing is a complex process involving patients and healthcare providers. The aim of the project was to examine the learning needs and preferences of healthcare providers and students to inform the development of an interprofessional deprescribing education program. METHODS: : An online survey of pharmacists, nurses, nurse practitioners, family physicians, and associated students practicing or studying in Nova Scotia was conducted. Respondents were recruited by purposive and snowball sampling to have at least five respondents within each professional/student group. Questions captured participant's self-reported comfort level and professional role for 12 deprescribing tasks and their learning preferences. RESULTS: : Sixty-nine respondents (46 healthcare providers and 23 students) completed the questionnaire. Average comfort levels for all 12 deprescribing tasks ranged from 40.22 to 78.90 of 100. Respondents reported their preferred deprescribing learning activities as watching videos and working through case studies. Healthcare providers preferred to learn asynchronously online, while students preferred a mix of online and in-person delivery. DISCUSSION: : Learning needs related to deprescribing tasks and roles were identified, as well as preferences for format and delivery of education. Development of an education program that can provide a shared understanding of collaborative deprescribing tailored to learner preferences may improve deprescribing in practice.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".