Self-rated health and barriers to healthcare in Ukraine: The pivotal role of gender and its intersections
Bibliographic record
Abstract
The ongoing health crisis in the Ukraine has persisted for 48 years with a clear division of gender-based outcomes as seen in the decline of male life expectancy and stagnation of female longevity. The purpose of this paper is to investigate differences in self-rated health and system barriers to health care applicable to gender and its intersections because of the differing negative health outcomes for men and women. Intersectionality theory provides an analytic framework for interpreting our results. Utilizing a nationwide sample of the Ukrainian population (N ¼ 1908), we found that low socioeconomic status (SES) women rate their health worse than men generally and any other socioeconomic group. Yet women also face the greatest barriers to health care until older ages when the ailments of men cause them to likewise face the obstacles. In reviewing the barrier to health care scale, one barrier—that of health care services being too expensive—dominated the responses with some 52.5 percent of the sample reporting it. Consequently, the greatest problem in Ukraine with respect to health reform reported by the population is the out-of-pocket costs for care in a system that is officially free. These costs, constituting some 40 percent of all national health expenditures, affect women and the aged the most.
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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 | 0.003 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".