Linkages between health systems and communities for chronic care: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Linkages between health systems and communities may leverage community assets to address unmet needs and provide services for improved continuity and coordination of care. However, there are limited examples of specific strategies for such linkages for chronic disease management. Guided by a local need from stakeholders, this scoping review aims to clarify and map methods and strategies for linkages between communities and health systems across chronic diseases, to inform future implementation efforts. METHODS AND ANALYSIS: The scoping review will be conducted following Arksey and O'Malley's methodological framework and latest Joanna Briggs Institute (JBI) guidelines, with continuous stakeholder engagement throughout. A structured literature search of records from January 2001 to April 2022 will be completed in MEDLINE/PubMed, CINAHL, EMBASE, PsycINFO, in addition to grey literature. Two reviewers will independently complete study selection following inclusion criteria reflecting population (chronic disease), concept (integrated care) and context (health systems and communities) and will chart the data. Data will be analysed using descriptive qualitative and quantitative methods, to map and operationalise the linkages between health systems and communities. ETHICS AND DISSEMINATION: The scoping review does not require ethics approval as it will examine and collect data from publicly available materials, and all stakeholder engagement will follow guidelines for patient and public involvement. Findings will be reported through a summarising list of considerations for different linkage strategies between health systems and community resources and implications for future research, practice and policy will be discussed and presented. The results will also be used to inform an integrated knowledge translation project to implement community-health system linkages to support chronic pain management. REGISTRATION NUMBER: 10.17605/OSF.IO/UTSN9.
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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 | 0.188 | 0.126 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.074 | 0.019 |
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".