444 Health care system accommodation of forcibly displaced migrants in Poland - challenges and adaptations
Bibliographic record
Abstract
Abstract PTH 3: Mental Health and Refugees 1, B307 (FCSH), September 3, 2025, 17:00 - 17:54 Aims The full-scale invasion of Ukraine by Russia led to one million individuals being forcibly displaced to Poland. The health care system represents an important place of contact with the refugee population. As such, it is critical to understand how these patients navigate through the health system and what facilitators and barriers are experienced throughout this process. Methods To address this, semi-structured interviews were conducted between October and December of 2024. Our purposive sample included 72 participants from nine large and small cities across Poland, including 30 refugees, 26 individuals in management roles (system planners, health care sector officials, leads in governmental and non-governmental organizations), and 16 individuals working in frontline roles (health care professionals, officers in governmental and non-governmental settings), representing different system areas and varied roles in the humanitarian crisis response. We analyzed the interviews in the context of the facilitators and barriers to accessing the health care system, as well as the system challenges and resulting adaptations from the perspectives of health care professionals and system planners. Results During the presentation, we will shed light on the main facilitators to accessing care that were identified by the participants. Nonetheless, barriers also persisted, including the ease of communication, the availability of medical records, and health care system differences that affected navigation through it. The participants described health care system pressures, which impacted its resilience, and the resulting adaptations. Conclusions Our research highlights areas of focus for improving access to care and alleviating the barriers. Implications for health system planners are suggested, such as the need for the continued assessment of the health status of refugees from Ukraine, the establishment of procedures and policies at the local and national levels with regular re-evaluations, and strong collaboration between all levels of government and with health care professionals and non-governmental organizations.
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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 | 0.003 | 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 | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".