Determinants of perceived health and unmet healthcare needs in universal healthcare systems with high gender equality
Bibliographic record
Abstract
BACKGROUND: Patient attitudes about health and healthcare have emerged as important outcomes to assess in clinical studies. Gender is increasingly recognized as an intersectional social construct that may influence health. Our objective was to determine potential sex differences in self-reported overall health and access to healthcare and whether those differences are influenced by individual social factors in two relatively similar countries. METHODS: Two public health surveys from countries with high gender equality (measured by UN GII) and universal healthcare systems, Canada (CCHS2014, n = 57,041) and Austria (AT-HIS2014, n = 15,212), were analysed. Perceived health was assessed on a scale of 1 (very bad) to 4 (very good) and perceived unmet healthcare needs was reported as a dichotomous variable (yes/no). Interactions between sex and social determinants (i.e. employment, education level, immigration and marital status) on outcomes were analysed. RESULTS: Individuals in both countries reported high perceived health (Scoring > 2, 85.0% in Canada, 79.9% in Austria) and a low percentage reported unmet healthcare needs (4.6% in Canada, 10.7% in Austria). In both countries, sex and several social factors were associated with high perceived health, and a sex-by-marital status interaction was observed, with a greater negative impact of divorce for men. Female sex was positively associated with unmet care needs in both countries, and sex-by-social factors interactions were only detected in Canada. CONCLUSIONS: The intersection of sex and social factors in influencing patient-relevant outcomes varies even among countries with similar healthcare and high gender equality.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".