Implementation, Challenges, and Outlook of an Intergenerational, Layperson-led, Health Coaching Program (HealthStart): A Pilot Case Study
Bibliographic record
Abstract
Background: As rapidly aging populations become a worldwide phenomenon, early detection and prompt management of chronic disease become essential to support healthy aging. Community-based health screenings, a key component of this strategy, often struggle with poor follow-up rates, limiting their long-term impact. Given the untapped potential of youth volunteers and the urgent need for a scalable approach to improve continuity of care post health screenings, we developed HealthStart: a structured, theory-based program that empowers these older adults to take greater ownership of their health and their chronic conditions with the support of youth community health volunteers (CHVs). Objective: This study aimed to describe the development, implementation, and early outcomes of HealthStart-an intergenerational, layperson-led health coaching program-and summarize operational lessons to guide similar models in Asian communities. Methods: HealthStart adopted an intergenerational service-learning approach modeled on a self-determination theory-based layperson-led health coaching framework. Each HealthStart team consisted of 1 health care volunteer (HCV) and 4 youth CHVs. All volunteers underwent blended training and were assessed for layperson-led health coaching readiness. Between September 2022 and June 2023 in Singapore, youth CHVs empowered adult participants aged 40 years and older after their health screening to (1) learn about their chronic diseases, (2) learn at least one digital health app, (3) enroll with a primary care provider, and (4) set a lifestyle goal (based on the Specific, Measurable, Achievable, Realistic/Relevant, and Time-bound [SMART] framework for goal setting) and achieve it. We used an implementation-focused case study design using descriptive statistics and volunteer-participant feedback to evaluate feasibility and outcomes. Results: Of 236 eligible individuals, 192 enrolled. Participants had a mean age of 67 (SD 9.6) years; 52.1% (n=100) of participants were female, with a majority of Chinese ethnicity, having completed primary or secondary school education, residing in self-owned flats, and living in 3-room public housing. Follow-up rate with primary care increased from 42.7% (82/148) preprogram to 84.5% (125/148) postprogram (χ21=43; P<.001). In total, 58 HCVs were recruited, comprising 26 nurses and 6 doctors, with the remainder as allied health professionals. A total of 33 were trained and deployed. The mean age of HCVs was 37 years old, and 24 (72.7%) were female. Furthermore, 149 youth CHVs were recruited, 138 trained, and 102 deployed. The mean age of the youth CHVs who were deployed was 24 years, and 75 (73.5%) were female. Reflections included the importance of volunteer competency and selection criteria, tiering of participant intervention, tapping on community assets, adoption of a social prescription framework, importance of alignment with population health policies, and cultivating intergenerational relationships. Conclusions: HealthStart demonstrates the feasibility and acceptability of a structured, intergenerational, layperson-led health coaching model embedded in primary care. We identify key lessons learned in the conceptualization and implementation of the program that may inform the design of similar volunteer-enabled initiatives for harnessing laypersons, an often-underused asset, to promote health in the community.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".