The impact of a naturally occurring retirement community supportive services program on older adult participants’ social networks: a qualitative study (Preprint)
Bibliographic record
Abstract
BACKGROUND The majority of older adults want to age in place, in their homes and communities. However, this can be challenging for many, frequently due to a lack of supports that allow for aging-in-place. Naturally occurring retirement community supportive services programs (NORC-SSPs) offer one approach to help older adults age in place. While qualitative studies have examined the experiences of NORC-SSP participants, little is known how participation in NORC-SSP programming affects participants’ social networks. OBJECTIVE This study aims to explore the experiences of thirteen NORC-SSP residents and how participating in NORC-SSP programming, specifically based on the Oasis model, influenced their social networks. METHODS Semi-structured qualitative interviews were conducted with participants in four NORC communities in Ontario, Canada. Social network theory informed the interview guide and thematic analysis. RESULTS Three main themes were identified from the interviews with Oasis participants: expansion and deepening of social networks, Oasis activities (something to do, someone to do it with), and the self-reported impact of Oasis on mental health and well-being (feeling and coping with life better). CONCLUSIONS Naturally occurring retirement communities offer an ideal opportunity to build strong communities that provide deep, meaningful social connections that expand their social networks. NORC-SSPS programs can support healthy aging and allow older adults to age-in-place.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".