Classifying the population by socioeconomic factors associated with support for policies to reduce social inequalities in health
Bibliographic record
Abstract
To examine citizens' agreement with policy options to reduce social inequalities in health and socio-demographic factors associated with support for these policies. A random digit dialling sample of 16 125 households with access to a landline telephone was conducted in Saskatoon, Canada in 2013. Saskatoon is located in the Canadian prairies and had a population of 222 189 in 2011. A total of 1002 individuals aged 18 or older answered a questionnaire indicating their support for policies to improve health equity. Socio-demographic variables of interest were household income, education, occupation and ethnicity. Latent class analysis and logistic regression analyses were conducted. The latent class analysis showed that 37% of respondents were in the selective agreement group, while 63% were in the high agreement group. The selective agreement group showed lower policy support compared with the high agreement group, in particular for guaranteed annual income, welfare for adults and parents with children, lower tuition for post-secondary students. In the final logistic regression model, socioeconomic factors associated with the likelihood of being in the selective agreement group were: age ≥40 years, male, Caucasian ethnicity and higher household income. Residents support for policies to reduce poverty and increase funding for education, creation of health promotion and disease prevention programmes. However, support for these policies is different across social groups.
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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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".