Unveiling the Impact and Diverse Contributions of Champions in Community Initiatives for Elderly Individuals with Chronic Conditions: An In‐Depth Examination through a Scoping Review
Bibliographic record
Abstract
In response to the global rise in chronic diseases among aging populations, healthcare systems are transitioning from acute care models to community‐based interventions tailored to the diverse needs of older adults. This study aimed to explore the role of champions in implementing community‐based interventions for older adults with chronic conditions to address the gap in understanding the specific functions and impact of champions in these contexts. A scoping review methodology used a six‐stage process aligned with the Arksey and O’Malley framework. After searching across 8 databases, a total of 10 studies met all inclusion criteria. The included studies were predominantly from the United States ( n = 6/10, 60%) and the United Kingdom ( n = 2/10, 20%). Most studies employed quantitative methodologies ( n = 6/10, 60%), describing and evaluating healthcare champion programs or investigating the quality of care for older adults. Community interventions targeted various chronic conditions (e.g., dementia) and health outcomes (e.g., medication reconciliation). Champions, ranging from 17 to 106 individuals, played diverse roles as professional healthcare providers or lay volunteers. They facilitated intervention delivery, advocated for change, participated in evaluation, and fostered collaboration within multidisciplinary teams, contributing significantly to program implementation and health information dissemination. Recognizing champions’ diverse roles, researchers and practitioners can tailor interventions to leverage their strengths and maximize their impact. Additionally, the review highlights the importance of clear definitions and operationalization of champions to ensure consistency across studies and interventions. Practitioners can use this knowledge to identify, train effectively, and support champions within their communities, ultimately enhancing the sustainability and success of interventions for older adults with chronic conditions.
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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 | 0.076 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".