Refugees' Experiences of Racism in Health Services: A Qualitative Inquiry on the Role of Affective Forecasting
Bibliographic record
Abstract
Racism in health services is a growing issue and affects refugees. This study's research question is: How do refugees' experiences of racism in health services influence their future behaviours and patient disengagement through affective forecasting? Research on the association between affective forecasting and refugees' previous experiences of racism in health services is limited, and several questions remain unanswered. This qualitative descriptive study included thirteen refugees with prior exposure to racism in Canadian health services. Semi-structured interviews were employed to gather data and thematic analysis to analyze this study's research question. This qualitative approach sheds light on emotional responses' influence on refugees' decision-making. The findings revealed that refugees' racist experiences in health services influenced their future behaviours and patient disengagement through the impact of their affective forecasts. This research provides practical implications for health service management to improve refugees' experiences in health services and directions for future research.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".