A Mixed Methods Study of Social Capital and Health Among Adults in Rural Ontario
Bibliographic record
Abstract
Social capital has shown the potential to benefit health, and therefore is an important concept to take up within nursing. However, the lack of consensus about how social capital should be defined and measured leads to challenges translating existing evidence into health promotion practice. Further, there is some literature suggesting that social capital may not benefit the health of rural residents in the same way as it does for urban residents. Therefore, there is a need for research that helps advance our conceptual knowledge of social capital while examining the concept and its impact on health for rural residents. This thesis involved a sequential explanatory mixed methods study to understand how rural residents experience social capital and how it impacts their health. In the first phase, I began with an exploratory factor analysis of the 2013 General Social Survey data. This revealed the underlying factors that made up social capital for urban and rural residents of Ontario. Logistic regression analysis indicated that four of the six social capital factors were positively associated with health. There were no differences between rural and urban residents in the factors revealed, nor in the influence of the factors on health, however rural residents scored higher on several social capital factors. In the second phase, interviews and focus groups in two rural Ontario communities helped explain the findings and explored how rural residents experienced social capital in their daily lives. The friendly and helpful social context helped elucidate why rural residents had high social capital scores, yet the structural context contributed to difficulties accessing social capital for some groups. Together, the data from both study phases help advance our knowledge of social capital with important implications for nursing practice.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".