Qualitative evaluation of a mandatory health insurance ‘wait period’ in a publicly funded health system: understanding health inequities for newcomer im/migrant women
Bibliographic record
Abstract
OBJECTIVES: To evaluate impacts of a residency-based waiting period for health insurance coverage on lived experiences of health and settlement for im/migrant women in British Columbia, Canada. DESIGN: The IRIS study is a mixed-methods, community-based, qualitative evaluation of recently arrived im/migrant women's access to sexual and reproductive care. In-depth, semistructured interviews were conducted by trained multilingual and multicultural interviewers with lived migration experience in the participant's preferred language. SETTING: Metro Vancouver, British Columbia, Canada from July 2018 to January 2020. PARTICIPANTS: Data collected from community focus groups (four groups, n=29) of both service providers and im/migrant women was used. Following this, qualitative interviews with service providers (n=10) and im/migrant women (n=47) were conducted. Eligible participants self-identified as women; were aged 18-49 and had arrived in Canada from another country. Eligible providers were employed in the health, social or legal sectors working with im/migrant women. RESULTS: The wait period resulted in mistrust and internalised stigma for racialised im/migrant women, for whom the policy resulted in feeling 'undeserving' of care. Resulting administrative burden produced delays and unmet need for care, particularly related to sexual and reproductive healthcare and children's health. Unexpected costs meant difficult choices between survival and care. Negative health outcomes included the inability to family plan, difficulties during pregnancy, as well as hardships related not being able to seek help for sick children. Community-based organisations provided support in many areas but could not fill all gaps produced by this policy. CONCLUSIONS: Findings highlight severe, yet commonly overlooked, health inequities produced by a mandatory health coverage wait period within a purportedly 'universal' healthcare system. Health system policies such as mandatory 'waiting periods' produce discriminatory and inequitable outcomes for im/migrant women. Policy reforms towards full 'healthcare for all' are urgently needed to affirm the health and human rights of all im/migrants.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".