Social capital and mental well-being of older people residing in a residential care facility in Durban, South Africa
Bibliographic record
Abstract
INTRODUCTION: Global increases in life expectancy are expected to continue, with accompanying physical and mental well-being challenges specifically for older people living in residential care settings. OBJECTIVE: The aim of the study was to investigate the association between mental well-being and social capital of older residents (60+ years) in an urban residential care facility in South Africa. METHOD: A descriptive survey was conducted with 103 residents living in a residential care facility in an urban environment in South Africa. The social capital framework from the Canadian Policy Research Initiative, the WHO-5 well-being index, the Kessler-6 measure of psychosocial distress, the OSLO-3 Social Support Scale and the Australian Bureau of Statistics Indigenous Health Questionnaire were used to develop the questionnaire. RESULTS: The WHO-5 showed moderate ratings of mental well-being for the standard scoring (>13) (62, 82.7%), but lower levels when using 'no negative ratings' (36; 50.6%). Significant differences in the primary network size, average closeness, self-efficacy and social support as well as the ability to confide in primary network was shown between residents with mentally well and unwell ratings. Logistic regression showed that the strongest predictor for mental well-being was participation in activities outside of the residence and having a primary network. CONCLUSION: The study confirms the association between social capital and mental well-being.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".