The impact of war on Primary Health Care in Ukraine: a cross-sectional survey and qualitative interviews with service providers
Bibliographic record
Abstract
BACKGROUND: Primary Health Care (PHC) is vital to supporting emergency preparedness and health care resilience. There is limited evidence of the impact of crises on PHC services and financing. We aimed to explore the impact of the full-scale invasion of Ukraine in February 2022 on PHC services in the country. METHODS: We used a mixed-methods approach. Survey data were collected using an online questionnaire sent to a sample (n = 86) of PHC providers in Ukraine in January-February 2023. Fifteen providers were then randomly selected for semi-structured interviews from among those that reported an impact of war and from those areas most affected by conflict. Interviews took place in March 2023. RESULTS: 37% of PHC providers reported being affected by the full-scale invasion. Qualitative data revealed greater impacts at the beginning of the invasion, to which facilities adapted by the time of the survey. The most reported disruptions were electricity cuts (76%) and currency depreciation/price increases (72%). The most reported increased medical need was cardiovascular disease (CVD; 58%) (with qualitative data suggesting an increase in CVD among younger patients) followed by mental illnesses and disorders (55%). 59% of PHC providers reported an increase in remote consultations. Among those facilities that reported a change in revenues, the nature of the change depended on the type of ownership. For example, only 9% of private providers reported increased revenues from humanitarian aid, while 79% (n = 58) of public providers indicated an increase in these sources. CONCLUSION: To continue strengthening Ukraine's PHC system, the benefit package must be aligned with clinical guidelines, particularly for CVD and mental health; increases in remote consultations should be closely monitored for quality; and payment systems must be adjusted to ensure equity of financing regardless of provider ownership. These findings offer insights for strengthening PHC and emergency-preparedness in other contexts.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".