Evaluating the sustainability and long-term outcomes of the Home Care Support Intervention Program (HoSIP) to reduce loneliness among community-dwelling older adults: A two-year follow-up study
Bibliographic record
Abstract
INTRODUCTION: Understanding the long-term effects of home care support programs on loneliness in older adults is crucial for optimizing service delivery and improving the quality of life and care. This research explores the Sustainability and Long-Term Outcomes of the Home Care Support Intervention Program (HoSIP) to Reduce Loneliness among Community-dwelling Older Adults: A two-year follow-up study. METHOD AND MATERIALS: This concurrent nested mixed-method study investigated the impact of HoSIP on older adults two years post-implementation. Quantitative data were collected on loneliness, social networks, perceived social support, quality of life, self-care ability, and general health. RAMNOVA analysis was used to analyze the results of univariate tests conducted at different points of measurement using SPSS version 23. Sixteen participants completed semi-structured individual interviews in-person and virtually. Conventional content analysis was undertaken using MAXQDA version 20. RESULTS: Sixteen older adults remained in the HoSIP program at the two-year post-test assessment (mean age 73.5 years + 6.6 years). The participants were predominantly female (81.3%). Over two years compared to baseline, a significant decline was observed in loneliness, social network, perceived social support, quality of life, self-care ability (p < 0.05) while no significant changes were observed for general health (p > 0.05). Three main categories, along with forth sub-categories, emerged from the data analysis. DISCUSSION: This study explored how a community-based program helped reduce loneliness in older adults. The results highlight the importance of involving older adults in designing programs to improve their overall well-being. These findings can guide future interventions to enhance the quality of life for older adults, potentially lowering healthcare costs and benefiting both individuals and governments. This program provides a framework for the development and implementation of sustained, community-based interventions directed by older adults. Given the potential impacts of sociocultural factors on the efficacy and longevity of such programs, these elements warrant careful consideration during the design phase of the similar interventions.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".