Refugee healthcare in British Columbia : health status and barriers for gorvernment asssised refugees in accessing healthcare
Bibliographic record
Abstract
Background: Government-Assisted Refugees (GARs) have greater health needs than other immigrants due to their pre-migration and Canadian resettlement experiences. There is a lack of detailed research into their health status and access to healthcare services. This thesis investigated factors associated with reported health, mental health problems, number of annual physician visits and difficulties obtaining healthcare from a sample of GARs. Methods: Secondary data analysis was conducted on data from a study of GARs in BC who attended the Bridge Refugee Clinic during the 26 month period from April 2005 to May 2007. Multivariate logistic regression was used to model the factors associated with excellent health, mental health problems, physician visits and difficulties obtaining healthcare. Results: There were 177 participants in the study. Excellent health was inversely associated with being female, having financial burden, having no English proficiency and having a diagnosed health condition. Factors associated with mental health problems were being female, west Asian, and having financial burden. Attending refugee clinics was inversely associated with reporting mental health problems. Factors associated with physician visits were unemployment, while not having English proficiency and no access to a regular doctor were inversely associated with the number of visits. Young Age, no access to a regular doctor and health region were associated with difficulties obtaining healthcare, while not being married had an inverse relationship with reporting difficulties. Conclusion: Findings highlight sex and English proficiency as important factors associated with GARs’ health and utilization of services. It is recommended that specialized health literacy classes, health programs and support groups for GARs, especially women, be developed. These interventions would benefit from active participation of ethnic communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".