Characteristics of programmes designed to link community-dwelling older adults in high-income countries from community to clinical sectors: a scoping review protocol
Bibliographic record
Abstract
Introduction Research on effectively navigating older adults into primary care is urgently needed. Community–clinic linkage models (CCLMs) aim to improve population health by linking the health and community sectors in order to improve patients’ access to healthcare and, ultimately, population health. However, research on community-based points of entry linking adults with untreated medical needs into the healthcare sector is nascent. CCLMs implemented for the general adult population are not necessarily accessible to older adults. Given the recency of the CCLM literature and the seeming rarity of CCLM interventions designed for older adults, it is appropriate to employ scoping review methodology in order to generate a comprehensive review of the available information on this topic. This protocol will inform a scoping review that reviews characteristics of community-based programmes that link older adults with the healthcare sector. Methods and analysis The present protocol was developed as per JBI Evidence Synthesis best practice guidance and reporting items for the development of scoping review protocols. The proposed scoping review will follow Levac and colleagues’ update to Arksey and O’Malley’s scoping review methodology. Healthcare access at the system and individual levels will be operationalised in data extraction and analysis in accordance with Levesque and colleagues’ Conceptual Framework of Access to Health. The protocol complies with Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. Beginning in August 2023 or later, citation databases (AgeLine (Ebsco); CINAHL Complete; MEDLINE (PubMed); Scopus Advanced (Elsevier); Social Services Abstracts (ProQuest); Web of Science Core Collection (Clarivate)) and grey literature (Google; American Public Health Association Annual Meeting Conference Proceedings; SIREN Evidence & Resource Library) will be searched. Ethics and dissemination The authors plan to disseminate their findings in conference proceedings and publication in a peer-reviewed journal and deposit extracted data in the Figshare depository. The study does not require Institutional Review Board approval. Registration details Protocol registered in Open Science Framework (DOI https://doi.org/10.17605/OSF.IO/2EF9D ).
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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 | 0.160 | 0.159 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.076 | 0.020 |
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".