Interventions to improve health care provider implementation and patient adherence of patients to recommendations on geriatric assessment and management in older adults: A scoping review protocol
Bibliographic record
Abstract
The world population is aging. Comprehensive Geriatric assessment (CGA) has been proven to improve the well-being of older adults. However, evidence suggests not all clinicians implement these recommendations in their practice; nor do all patients adhere to them. Currently, there is no up-to-date review of interventions that can improve older adults' adherence to CGA recommendations and health care provider/clinician implementation of those recommendations. The objective of this scoping review protocol is to describe the methodology of the scoping review with the aim to identify interventions that have been tested to improve adherence to CGA recommendations. We will use the Arksey and O'Malley framework and subsequent extension by Levac and colleagues to complete the scoping review. We searched OVID MEDLINE, OVID Embase, EBSCO CINAHL, APA PsychInfo, and Cochrane CENTRAL databases from inception to November 14, 2024, and will include a review of reference lists of included studies. Studies eligible for inclusion are studies of any design that examined one or more interventions aiming to improve clinician implementation of and patient adherence to CGA in any clinical setting. We will use standard methods for study selection, data abstraction, assessment of methodological quality of individual studies, and data synthesis. Results will be analyzed and reported using descriptive numerical summaries and narrative analysis. Findings from the scoping review will be published in a manuscript and presented at scientific conferences.
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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 | 0.079 | 0.070 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.068 | 0.011 |
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".