Receipt of adequate prenatal care for privately sponsored versus government-assisted refugees in Ontario, Canada: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Canada has 2 main streams of resettlement: government-assisted refugees and privately sponsored refugees, whereby citizens can privately sponsor refugees and provide resettlement services, including health care navigation. Our objective was to compare receipt of adequate prenatal care among privately sponsored and government-assisted refugees. METHODS: This population-based study used linked health administrative and demographic databases. We included all resettled refugees classified as female who landed in Ontario, Canada, between April 2002 and May 2017, and who had a live birth or stillbirth conceived at least 365 days after their landing date. Our primary outcome - adequacy of prenatal care - was a composite that comprised receipt of a first-trimester prenatal visit, the number of prenatal care visits recommended by the Society of Obstetricians and Gynaecologists of Canada and a prenatal fetal anatomy ultrasound. We accounted for potential confounding with inverse probability of treatment weighting, using a propensity score. RESULTS: We included 2775 government-assisted and 2374 privately sponsored refugees. Compared with privately sponsored refugees (62.3% v. 69.3%), government-assisted refugees received adequate prenatal care less often, with a weighted relative risk of 0.93 (95% confidence interval 0.88-0.95). INTERPRETATION: Among refugees resettled to Canada, a government-assisted resettlement model was associated with receiving less adequate prenatal care than a private sponsorship model. Government-assisted refugees may benefit from additional support in navigating health care beyond the first year after arrival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".