Engaging im/migrant communities in cross-sectoral health and immigration data linkage research.
Bibliographic record
Abstract
ObjectivesWe aim to respond to health care barriers experienced by immigrant and migrant (im/migrant) communities through community-engaged research using population-based multi-sectoral linked health and immigration data, alongside qualitative methods. We describe lessons learned with respect to analytic choices and interpretation of findings from data linkage research. ApproachWe linked Canadian federal immigration data and health data from the province of British Columbia to analyze access to health care services during the COVID-19 pandemic. Immigration data include date and class of arrival, level of education, language ability at arrival, countries of birth and origin, and other personal characteristics. Provinces also collect documentation of immigration status as part of ascertaining health insurance eligibility, data not previously used for research. Planning and carrying out this analysis involved people who come from different countries and have different immigration journeys, such as people with precarious im/migration status, refugees, workers and students. ResultsFindings underscore that care should be taken in choosing categories to group people using administrative immigration systems data that are relevant to research questions, considering class of arrival, current status, time since arrival, and language ability, alongside intersecting characteristics. In studying COVID-19 infection and access to care, current status (temporary or permanent) was particularly important, as this is tied to both workplace protections/risks and access to care. Time since arrival in Canada and language ability were important in examining questions related to health system navigation, including access to virtual and in-person care. Immigration information recorded at time of registration for provincial insurance offers a new opportunity to include immigration data in analysis, and is particularly helpful in studying impacts of temporary status. ConclusionA strength of linked immigration data is that it directly captures administrative categories that are modifiable and that structurally determine health. In interpreting analysis we must emphasize that immigration records and class captured at time of registration for health insurance reflect administratively imposed categories, but may not reflect identities.
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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 | 0.233 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".