AN INTERPROFESSIONAL APPROACH TO DEPRESCRIBING: A CURRICULAR FRAMEWORK
Bibliographic record
Abstract
Abstract Deprescribing is an important approach for managing polypharmacy and reducing harm from potentially inappropriate medications. Healthcare professionals identify barriers to deprescribing, including lack of knowledge and skill. This is not surprising as pre-licensure education does not consistently incorporate components of deprescribing into curricula. As such, there is a clear need to consider how to promote deprescribing competencies, teach related knowledge and skills and assess learning outcomes. The Canadian Deprescribing Network (CaDeN) Health Care Professional Committee undertook a consensus process to develop a proposed competency framework that describes essential knowledge, teaching strategies, and assessment protocols to promote deprescribing skills and advocate for consistent education about deprescribing principles and practices. The framework is informed by the deprescribing process, which includes gathering and interpreting patients’ medication history and clinical information within their context, using tools that help identify potentially inappropriate medications, weighing potential benefit and harm of continuing or deprescribing medications, using shared decision-making to make decisions about deprescribing, communicating deprescribing and monitoring plans, and monitoring progress and outcomes. The competency framework considers interprofessional learning and how to involve patients and care partners in deprescribing decisions. Integrating deprescribing competencies in healthcare curricula requires an intentional and structured approach across all years of the program, focusing on interprofessional collaboration. Learning activities should be active and practical, progressing from early to advanced learner skills and include integration of deprescribing during experiential education. This framework includes a review of the competencies, learning outcomes, and assessment strategies, with a discussion of strategies to incorporate interprofessional learning activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".