“This is what labels you”: Examining the structural context of how limited English proficiency and experiences with interpretation services interact to shape health and health access for im/migrant women in Metro Vancouver, British Columbia
Bibliographic record
Abstract
Considerable research has documented negative health outcomes of ‘language barriers’ for im/migrants in destination countries. There is a crucial need for research underpinned by structural and intersectional frameworks that center im/migrant women’s lived experiences to inform interventions that move beyond the individual-level towards systemic, equity-oriented change. This study analyzed qualitative data from focus groups (4, N=29) and individual interviews with im/migrant women (N=49) and providers (N=10) conducted from July 2018 – February 2020 in Metro Vancouver, British Columbia. Moving beyond conceptualizations of language as a ‘barrier’, narratives revealed how unmet communication needs for im/migrant women operated as a form of systemic discrimination. Responsibility for communication often rested on im/migrant women, relegating them to a second tier of care. Best practices for interpretation included a holistic approach that went beyond availability of language-concordant options towards im/migrant-sensitive models that accommodate converging effects of language, im/migration status, systemic racism, and gender.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.006 | 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".