Unmet healthcare needs among migrants without medical insurance in Montreal, Canada
Bibliographic record
Abstract
While access to healthcare for permanent residents in Canada is well known, this is not the case for migrants without healthcare coverage. This is the first large-scale study that examines the unmet healthcare needs of migrants without healthcare coverage in Montreal. 806 participants were recruited: 436 in the community and 370 at the NGO clinic. Proportions of individuals reporting unmet healthcare needs were similar (68.4% vs. 69.8%). The main reason invoked for these unmet needs was lacking money (80.6%). Situations of not working or studying, not having had enough food in the past 12 months, not having a medical prescription to get medication and having had a workplace injury were all significantly associated with higher odds of having unmet healthcare needs. Unmet healthcare needs were more frequent among migrants without healthcare coverage than among recent immigrants or the citizens with health healthcare coverage (69%, 26%, 16%). Canada must take measures to enable these individuals to have access to healthcare according to their needs in order to reduce the risk of worsening their health status, something that may have an impact on the healthcare system and population health. The Government of Quebec announced that all individuals without any healthcare coverage will have access to COVID-19 related health care. We hope that this right, the application of which is not yet obvious, can continue after the pandemic for all health care.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".