Facilitating refugees’ access to family doctors
Bibliographic record
Abstract
Purpose – The purpose of this paper is to describe the patient level characteristics of government-assisted refugees (GARs) who had acquired family doctors after leaving specialized refugee clinics (RC). Design/methodology/approach – A cross-sectional telephone survey of GARs households, three to six years after arrival to British Columbia, that used logistic regression to identify GAR characteristics associated with having a family doctor compared to having no family doctor or remaining at a RC. Findings – Contact rate was 52 percent. Of 177 interviewed GARs who spoke 24 languages, only 61 percent had secured a family doctor. Only 57 percent were educated; 46 percent spoke English and 40 percent worked consistently. Central Asian or African origin was associated with having a family doctor (OR 10.6 (95 percent CI 3.1-36.8) for RC; OR 10.3 (95 percent CI 2.2-47.8) for no family doctor). Other significant characteristics in the comparison with GARs at a RC included English proficiency (OR 15.6 (95 percent CI 4.3-56.9)), and female sex (OR 4.0 (95 percent CI 1.4-1.1)). When compared to those with no family doctor, additional significant characteristics included Health Authority A compared to B (OR 8.9, 95 percent CI 1.4-55.6) and having recently visited a doctor (OR 7.7 (95 percent CI 1.9-30.7)). Research limitations/implications – The results of this study are limited to a specific environment and the low contact rate may have resulted in bias. Originality/value – This study described characteristics of GARs who had successfully transitioned to a family doctor and those who had not. This population is rarely captured in studies because they are difficult to contact, ethnically diverse and not proficient in English.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".