The Impact of Immigration Status on the Experience of Obstetric Care in Quebec
Bibliographic record
Abstract
To compare the experience of obstetric care in Quebec between immigrants and Canada-born persons.A cross-sectional survey was conducted among individuals who received obstetric care in Quebec between 2016 and 2023. Participants, recruited mainly via social media, completed an online questionnaire between July and December 2023. Obstetric care experiences, including autonomy measured by the MADM scale (Mothers Autonomy in Decision Making), mistreatment assessed by the MIST index (Pregnant Persons Experience of Mistreatment by Providers Index), discrimination, access to care, and satisfaction towards interpersonal skills of healthcare providers, were compared between immigrant and Canada-born participants. Among 686 participants, 11.2% were immigrants, among which 69.0% had permanent status and 14.3% temporary status. There was no significant difference in MADM scale between immigrant and Canada-born participants (p = 0.903), but immigrants with temporary status were 9.25 times more likely to have low autonomy (CI: 1.06-80.77) compared to those born in Canada controlling for confounding variables. According to the MIST index, at least one of disrespectful behaviors was reported by 28.6% of immigrants and 35.8% Canada-born individuals (p = 0.172). Among immigrant participants, 2.6% reported being treated less favorably due to their ethnic, cultural, or linguistic background, compared to 1.5% of Canada-born participants (p = 0.09). Additionally, 9.1% of immigrants had to paid for obstetric care personally versus 4.4% of Canada-born (p = 0.173). Our study highlights immigration status as a key differentiating factor in obstetric care, with lower autonomy among temporary immigrants, while no significant differences were found between permanent immigrants and Canadian-born participants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".