Encouraging understanding or increasing prejudices: A cross-sectional survey of institutional influence on health personnel attitudes about refugee claimants' access to health care
Bibliographic record
Abstract
BACKGROUND: This paper investigates the personal, professional and institutional predictors of health institution personnel's attitudes regarding access to healthcare for refugee claimants in Canada. METHODS: In Montreal, the staff of five hospitals and two primary care centres (n = 1772) completed an online questionnaire documenting demographics, occupation, exposure to refugee claimant patients, and attitudes regarding healthcare access for refugee claimants. We used structural equations modeling to investigate the associations between professional and institutional factors with latent functions of positive and negative attitudes toward refugee's access to healthcare. RESULTS: Younger participants, social workers, participants from primary care centres, and from 1st migrant generation had the lowest scores of negative attitudes. Respondents who experienced contact with refugees had lower scores of negative attitudes (B = -14% standard deviation [SD]; 95% CI: -24, -4%). However, direct contact with refugees increased scores of negative attitudes in the institution with the most negative attitudes by 36% SD (95% CI: 1, 71%). INTERPRETATION: Findings suggest that institutions influence individuals' attitudes about refugee claimants' access to health care and that, in an institutional context of negative attitudes, contact with refugees may further confirm negative perceptions about this vulnerable group.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".