The Development of a Deprescribing Competency Framework in Geriatric Nursing Education
Bibliographic record
Abstract
The purpose of this article is to report the literature review findings of our larger deprescribing initiative, with the goal of developing a competency framework about deprescribing to be incorporated into the future geriatric nursing education curriculum. A literature review was conducted to examine the facilitators and barriers faced by nurses with regard to the process of deprescribing for older adults, and the development of deprescribing competency in nursing education. We adopted the seven steps of the Comprehensive Literature Review Process Model, which is sub-divided into the following three phases (a) Exploration; (b) Interpretation; and (c) Communication. A total of 24 peer-reviewed documents revealed three major facilitating factors: (a) Effective education and training in deprescribing; (b) Need for continuing education and professional development in medication optimization; and (c) Benefits of multi-disciplinary involvement in medication management.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".