A systems approach to identifying the challenges of implementing deprescribing in older adults across different health-care settings and countries: a narrative review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: There is increasing recognition of the need for deprescribing of inappropriate medications in older adults. However, efforts to encourage implementation of deprescribing in clinical practice have resulted in mixed results across settings and countries.Area covered: Searches were conducted in PubMed, Embase, and Google Scholar in June 2019. Reference lists, citation checking, and personal reference libraries were also utilized. Studies capturing the main challenges of, and opportunities for, implementing deprescribing into clinical practice across selected health-care settings internationally, and international deprescribing-orientated policies were included and summarized in this narrative review.Expert opinion: Deprescribing intervention studies are inherently heterogeneous because of the complexity of interventions employed and often do not reflect the real-world. Further research investigating enhanced implementation of deprescribing into clinical practice and across health-care settings is required. Process evaluations in deprescribing intervention studies are needed to determine the contextual factors that are important to the translation of the interventions in the real-world. Deprescribing interventions may need to be individually tailored to target the unique barriers and opportunities to deprescribing in different clinical settings. Introduction of national policies to encourage deprescribing may be beneficial, but need to be evaluated to determine if there are any unintended consequences.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it