Effectiveness of knowledge translation tools addressing multiple high-burden chronic diseases affecting older adults: protocol for a systematic review alongside a realist review
Bibliographic record
Abstract
INTRODUCTION: The burden of chronic disease is a global phenomenon, particularly among people aged 65 years and older. More than half of older adults have more than one chronic disease and their care is not optimal. Chronic disease management (CDM) tools have the potential to meet this challenge but they are primarily focused on a single disease, which fails to address the growing number of seniors with multiple chronic conditions. METHODS AND ANALYSIS: We will conduct a systematic review alongside a realist review to identify effective CDM tools that integrate one or more high-burden chronic diseases affecting older adults and to better understand for whom, under what circumstances, how and why they produce their outcomes. We will search MEDLINE, EMBASE, CINAHL, AgeLine and the Cochrane Library for experimental, quasi-experimental, observational and qualitative studies in any language investigating CDM tools that facilitate optimal disease management in one or more high-burden chronic diseases affecting adults aged ≥65 years. Study selection will involve calibration of reviewers to ensure reliability of screening and duplicate assessment of articles. Data abstraction and risk of bias assessment will also be performed independently. Analysis will include descriptive summaries of study and appraisal characteristics, effectiveness of each CDM tool (meta-analysis if appropriate); and a realist programme theory will be developed and refined to explain the outcome patterns within the included studies. ETHICS AND DISSEMINATION: Ethics approval is not required for this study. We anticipate that our findings, pertaining to gaps in care across high-burden chronic diseases affecting seniors and highlighting specific areas that may require more research, will be of interest to a wide range of knowledge users and stakeholders. We will publish and present our findings widely, and also plan more active dissemination strategies such as workshops with our key stakeholders. TRIAL REGISTRATION NUMBER: Our protocol is registered with PROSPERO (registration number CRD42014014489).
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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 | 0.165 | 0.209 |
| Meta-epidemiology (narrow) | 0.009 | 0.008 |
| Meta-epidemiology (broad) | 0.021 | 0.026 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.065 | 0.014 |
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".