Social Networks and the Probability of Having a Regular Family Doctor
Bibliographic record
Abstract
This paper contributes to te literature on the determinants of healthcare utilization by examining how social network affect whether or not the individuals has a regular doctor. This question is particularly important in situations where the supply of family psysicians is clearly a constraint - as is the case for most jurisdictions in Canada. Having a regular doctor has been shown to have an impact on the health of an individual (McIsaac et al. 2001; Sanmartin et al., 2004; Sanmartin & Ross, 2006). This study uses the Canadian Community Health Surveys from 2000 to 2010 to examine the relationship between having a regular doctor and social networks. Three measures of social networks are used which include: sense of belonging to the local community, how often an individual has someone to confine in, and number of close friends and relatives. A probit model is employed to estimate the impact of a variety of demographic and socio-economic variables, as well as social networks, on the probability of having a regular doctor. Some evidence is found that there is a relationship between social networks and having a regular doctor.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".