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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".