Aging, Social Capital, and Utilization of Health Services in Canada
Bibliographic record
Abstract
This paper seeks to understand the relationship between aging, social capital, and utilization of physician services. We use cross-sectional data from the 2001 Canadian Community Health Survey (wave 1.1) and the 2001 Census to estimate a two-part model of GP utilization (visits) with special attention paid to the impact of community and individual social capital. We control for the effect of diet, substance abuse (smoking, alcohol consumption), immigrant status, community migration levels, baseline health status (# of chronic conditions), a regular source of primary care, income, education, and labour force participation. Individual social capital (ISC) is measured on a four-point likert scale regarding how connected a person is to their community. Community Social Capital (CSC) is measured at the metropolitan level using employment levels in religious and community-based organizations [NAICS code 813XX] - a.k.a., Petris Index). Community social capital exhibits an impact on health care utilization independent of the effect of individual social capital. However, the impact varies by age group. For those over the age of 65, a higher level of CSC is associated with a lower propensity to visit a GP while ISC operated in the opposite direction. Likelihood ratio test indicated that the effects of CSC and ISC were generally insignificant for those under 65. The same pattern was observed with regard to the OLS results: a 1% increase in the Petris CSC index lead to a 1.1 - 1.2% decrease in GP visits among the population 65 , but little impact for younger cohorts. The impact from increased ISC was in the opposite direction and also smaller in magnitude than for CSC. The results of the quantile regression suggest that the effect of CSC may be most prominent in the middle ranges of utilization while the impact of ISC is most prominent at the lower end of the utilization distribution. Each form of social capital most likely operates through a different mechanism: ISC perhaps serves an enabling role by improving access (e.g. transportation services) while CSC perhaps serves to obviate some types of physician visits possibly those that generally involve mainly counseling/caring services most important to seniors.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".