Investigating the relationship between social capital and self-rated health in South Africa
Bibliographic record
Abstract
Much research has examined the relationship between social capital and self-rated health in developed countries. Few studies, however, have investigated this important relationship in developing countries. This study examined this research gap using data from the National Income Dynamics Study (NIDS), the first nationally representative panel study in South Africa. Information regarding social capital norms of reciprocity, association activity, trust and group membership was assessed in NIDS. Self-rated health was collected at Wave 1 in 2008, and Wave 2 in 2010 2011. The final sample consisted of 8866 respondents. Mixed effects models were fitted to predict self-rated health in Wave 2, using lagged covariates (from Wave 1). The results indicated that individual personalised trust, individual community service group membership and neighbourhood personalised trust were beneficial to self-rated health. Reciprocity, associational activity and other types of group memberships were not found to be significantly associated with self-rated health. Results indicate that both individualand contextual-level social capital are associated with self-rated health. Policy makers in South Africa may want to consider social capital, in addition to other well-known social determinants of health, when implementing policies to improve the health of its population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".