Newcomer Women's Experiences with Perinatal Care During the Three-Month Health Insurance Waiting Period in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The three-month health insurance waiting period in Ontario reinforces health inequities for newcomer women and their babies. Little is known about the systemic factors that shape newcomer women's experiences during the OHIP waiting period. PURPOSE: To examine the factors that shaped newcomer women's experiences with perinatal care during the three-month health insurance waiting period in Ontario, Canada. METHODS: This qualitative study was informed by an intersectional framework, and guided by a critical ethnographic method. Individual interviews were conducted with four newcomer women and three perinatal healthcare professionals. Participant observations at recruitment and interview sites were integral to the study design. RESULTS: The key systemic factors that shaped newcomer women's experiences with perinatal care included social identity, migration, and the healthcare system. Social identities related to gender, race, and socio-economic status intersected to form a social location, which converged with newcomer women's experiences of social isolation and exclusion. These experiences, in turn, intersected with Ontario's problematic perinatal health services. Together, these factors form systems of oppression for newcomer women in the perinatal period. CONCLUSIONS: Given the health inequities that can result from these systems of oppression, it is important to adopt an upstream approach that is informed by the Human Rights Code of Ontario to improve accessibility to and the experiences of perinatal care for newcomer women.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".