Caring for pregnant refugee women in a turbulent policy landscape: perspectives of health care professionals in Calgary, Alberta
Bibliographic record
Abstract
Abstract Background Female refugees can be a vulnerable population, often having suffered through traumatic events that pose risks to their health, especially during pregnancy. Pregnancy can be an entry point into the health care system, providing health care professionals the opportunity to gain women’s trust, connect refugees with resources, and optimize the health of mother and child. Policies surrounding the provision and funding of health care services to refugees can impact access to and quality of care. The aim of our study was to understand the experiences of health care professionals caring for pregnant refugee women in Calgary, AB, taking into consideration recent contextual changes to the refugee landscape in Canada. Methods We conducted ten semi-structured interviews with health care professionals who provided regular care for pregnant refugee women at a refugee health clinic and major hospital in Calgary, Alberta. Interviews were recorded, transcribed, and analyzed using an interpretive description methodology. Results Health care providers described several barriers when caring for pregnant refugees, including language barriers, difficulty navigating the health care system, and cultural barriers such as managing traditional gender dynamics, only wanting a female provider and differences in medical practices. Providers managed these barriers through strategies including using a team-based approach to care, coordinating the patient’s care with other services, and addressing both the medical and social needs of the patient. The federal funding cuts added additional challenges, as many refugees were left without adequate health coverage and the system was complicated to understand. Health care providers developed creative strategies to maximize coverage for their patients including paying out of pocket or relying on donations to care for uninsured refugees. Finally, the recent Syrian refugee influx has increased the demand on service providers and further strained already limited resources. Conclusion Health care providers caring for pregnant refugee women faced complex cultural and system-level barriers, and used multiple strategies to address these barriers. Additional system strains add extra pressure on health care professionals, requiring them to quickly adjust and accommodate for new demands.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.015 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".