Bibliographic record
Abstract
Ukraine is experiencing an internationally recognized health crisis. Rural residents bear a disproportionate burden of this crisis due to challenging socioeconomic conditions in villages and disintegration of rural health care. Currently, Ukraine is piloting its first major health care reform, focused on changing the health care system from the patient-specialist to the primary care model. The reform has been critiqued for lack of attention to the social determinants of health and insufficient public consultation. Also, little is known from the Ukrainian rural population about their health concerns. This study attempts to bridge this knowledge gap in rural health policy in Ukraine. Fourteen interviews with health professionals and local representatives were conducted to identify the intermediate results of the pilot reform and barriers in accessing primary care in villages. The secondary analysis of five community consultations (funded by the Canadian Institutes of Health Research, PI – Dr. Olena Hankivsky) presents a picture of health concerns in rural Ukraine. Together, these findings inform the final recommendation, which combines community-level health initiatives, the creation of the Rural Health Framework and reviving the profession of a feldsher (analogous to a nurse practitioner) in Ukraine.
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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 | 0.044 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".