Prevalence of selected preventable and treatable diseases among government-assisted refugees: Implications for primary care providers.
Bibliographic record
Abstract
OBJECTIVE: To discover the prevalence of 4 preventable and treatable diseases among newly arriving refugees. DESIGN: Retrospective cohort study. SETTING: An immigrant-friendly family medicine centre in Ottawa, Ont, that offers newly arriving refugees a clinical preventive program following a specially designed protocol. PARTICIPANTS: A total of 112 adult government-assisted refugees seen during 2004 and 2005 within 6 months of arrival. MAIN OUTCOME MEASURES: Demographic information and prevalence of HIV infection, latent tuberculosis (TB), chronic hepatitis B surface antigen-positive status, and intestinal parasites. RESULTS: Descriptive analysis revealed that 71% of the adults were younger than 35 years and 83% of them had come from sub-Saharan Africa. Disease prevalence rates were 6.3% for HIV (95% confidence interval [CI] 1.8 to 10.8), 49.5% for latent TB (95% CI 39.5 to 49.8), 5.4% for chronic hepatitis B surface antigen-positive status (95% CI 1.2 to 9.5), and 13.6% for intestinal parasites (95% CI 7.2 to 20.0). Most refugees (83%) successfully completed the preventive care program. Performing chi(2) analysis revealed a statistically significant higher risk of latent TB among the men (P < .032). Most of the women had never had a Papanicolaou test. CONCLUSION: Refugees are a vulnerable population with unique, but often preventable or treatable, health issues. This study demonstrated substantial differences in the prevalence of HIV, TB, chronic hepatitis B, and intestinal parasites between government-assisted refugees and Canadian residents. These health disparities and the emerging field of health settlement are new challenges for family physicians and other primary health care providers.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".