Identifying and understanding the health and social care needs of older adults with multiple chronic conditions and their caregivers: a protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: People are living longer; however, they are not necessarily experiencing good health and well-being as they age. Many older adults live with multiple chronic conditions (MCC), and complex health issues, which adversely affect their day-to-day functioning and overall quality of life. As a result, they frequently rely on the support of friend and/or family caregivers. Caregivers of older adults with MCC often face challenges to their own well-being and also require support. Currently, not enough is known about the health and social care needs of older adults with MCC and the needs of their caregivers or how best to identify and meet these needs. This study will examine and synthesise the literature on the needs of older adults with MCC and those of their caregivers, and identify gaps in evidence and directions for further research. METHODS AND ANALYSIS: We will conduct a scoping review of the peer-reviewed and grey literature using the updated Arksey and O'Malley framework. The literature will be identified using a multidatabase and grey literature search strategy developed by a health sciences librarian. Papers, reports and other materials addressing the health and social care needs of older adults and their friend/family caregivers will be included. Search results will be screened, independently, by two reviewers, and data will be abstracted from included literature and charted in duplicate. ETHICS AND DISSEMINATION: This scoping review does not require ethics approval. We anticipate that study findings will inform novel strategies for identifying and ascertaining the health and social care needs of older adults living with MCC and those of their caregivers. Working with knowledge-user members of our team, we will prepare materials and presentations to disseminate findings to relevant stakeholder and end-user groups at local, national and international levels. We will also publish our findings in a peer-reviewed journal.
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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 | 0.157 | 0.138 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.068 | 0.018 |
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".