A DEPRESCRIBING CURRICULAR FRAMEWORK USING AN INTER-PROFESSIONAL APPROACH: IMPLICATIONS FOR NURSING EDUCATION
Bibliographic record
Abstract
Abstract Background Deprescribing is an important component of managing polypharmacy and reducing harm from potentially inappropriate medications. Current undergraduate nursing education does not consistently incorporate components of deprescribing into curricula. It is essential to bridge the gap in promoting deprescribing competencies, teach related knowledge and skills and assess learning outcomes. The purpose of this presentation is to engage nursing educators who teach geriatrics to identify and implement a curriculum framework for deprescribing. Methodology The Canadian Medication Appropriateness and Deprescribing Network (CaDeN) Healthcare Professional Committee undertook a consensus approach to developing competencies for deprescribing, along with literature review and analysis of prescribing competencies. The authors outlined the required knowledge and skills related to the competencies, with recommended teaching and assessment strategies. Results The seven deprescribing competencies include: 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 about deprescribing, communicating deprescribing and monitoring plans, and monitoring progress and outcomes. Integrating deprescribing competencies in nursing 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 through experiential education. Conclusion This presentation provides implications for the nursing profession to apply deprescribing competencies at different learner levels, using appropriate learning and assessment outcomes, and strategies in which deprescribing competencies could be achieved in an interprofessional setting.
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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 | 0.026 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".