Exploring the Impact of Health Care Provider Cultural Competence on New Immigrant Health-Related Quality of Life: A Cross-Sectional Study of Canadian Newcomers
Bibliographic record
Abstract
Introduction: New immigrants underutilize health care because of multiple barriers. Although culturally competent health care improves access, it is typically assessed by providers, not newcomers whose perceptions matter most. Methodology: Surveys that included measures of cultural competence and health-related quality of life (QOL) were completed by 117 new immigrants in Windsor, Ontario, Canada. A series of stepwise linear regression analyses were conducted to identify independent predictors of QOL and its four domains: physical health, psychological, social relationships, and environment. Results: Our adjusted results suggest that experiences of discrimination was negatively associated with overall QOL (β = −.313; p < .001) and its psychological (β = −.318; p < .001), social (β = −.177; p = .048), and environmental (β = −.408; p < .001) domains. Discussion: Discrimination negatively influences new immigrant QOL. Provider cultural competency training should emphasize the influence of provider discrimination on immigrant health and explore learners’ values and biases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".