Socioeconomic disparities in health care use: Does universal coverage reduce inequalities in health?
Bibliographic record
Abstract
BACKGROUND: Despite enormous public sector expenditures, the effectiveness of universal coverage for health care in reducing socioeconomic disparities in health has received little attention. STUDY OBJECTIVE: s: To evaluate whether universal coverage for health care reduces socioeconomic disparities in health. DESIGN: Information on participants of the 1990 Nova Scotia Nutrition Survey was linked with eight years of administrative health services data and mortality. The authors first examined whether lower socioeconomic groups use more health services, as would be expected given their poorer health status. They then investigated to what extent differential use of health services modifies socioeconomic disparities in mortality. Finally, the authors evaluated health services use in the last years of life when health is poor regardless of a person's socioeconomic background. SETTING: The Canadian province of Nova Scotia, which provides universal health care coverage to all residents. PARTICIPANTS: 1816 non-institutionalised adults, aged 18-75 years, from a two stage cluster sample stratified by age, gender, and region. MAIN RESULTS: People with lower socioeconomic background used comparatively more family physician and hospital services, in such a way as to ameliorate the socioeconomic differences in mortality. In contrast, specialist services were comparatively underused by people in lower socioeconomic groups. In the last three years of life, use of specialist services was significantly higher in the highest income group. CONCLUSIONS: Universal coverage of family physician and hospital services ameliorate the socioeconomic differences in mortality. However, specialist services are underused in lower socioeconomic groups, bearing the potential to widen the socioeconomic gap in health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".