Utilization of Primary and Secondary Medical Care among Disadvantaged Populations: A Log-Linear Model Analysis
Bibliographic record
Abstract
AIM: We examined how, where an overall population is covered by universal health insurance, characteristics of disadvantaged populations interact to influence inequality in primary and secondary medical care utilization. SUBJECTS & METHODS: Disadvantaged populations, the focus of the study, were defined as populations who have lower socio-economic status (SES), who are elderly and/or reside in a peripheral area. Data from the 2009 Israeli National Health Survey were analysed using log-linear models to estimate utilization of medical care. RESULTS: The main findings were: a) pro-poor utilization of primary medical care among elderly populations, with higher odds ratios for low SES populations in the periphery; (b) lack of interaction between SES and primary medical care utilization among younger populations, between SES and secondary medical care utilization among the elderly and pro-rich utilization of secondary medical care among younger populations who did not regularly visit general practitioners (GP); (c) the odds ratios of secondary medical care utilization increased as SES decreased for both elderly and younger populations who also regularly visited a GP. CONCLUSION: Potential policy implications for disadvantaged populations, regarding possible inequality in primary and secondary medical care utilization, can be drawn using log-linear model analysis of interactions among characteristics (SES, age, location) of disadvantaged populations.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".