Health status and care utilization among Afghan refugees recently resettled in Calgary, Canada between 2011–2020
Bibliographic record
Abstract
BACKGROUND: The United States and Canada have resettled over 120,000 Afghan refugees since August 2021, but sociodemographic and health status data remains sparse with investigations often limited to refugee entrance exams, standardized health screenings, or acute health settings. METHODS: This retrospective community-engaged cross-sectional study investigated Afghan patients who received care between January 1, 2011 and December 31, 2020 at an interdisciplinary specialized refugee clinic in Calgary, Canada that provides care to newly arrived refugees. Two reviewers independently extracted and manually verified sociodemographic factors, medical diagnoses, and clinic utilization variables from patients’ electronic medical records, then coded patient diagnoses into ICD-10 codes and chapter groups. Diagnosis frequencies were calculated and stratified by age group and sex. We corroborated these findings with Afghan refugee co-investigators. FINDINGS: Among 402 Afghan refugee patients, 228 were adults (mean age 34.2 [SD 13] years), and 174 were children (mean age 7.5 [SD 5.4] years). We identified 1535 total individual diagnoses and classified them into 382 unique ICD-10 codes. Patients had a median 2 diagnoses each [IQR 0–6], 4 clinic visits across primary, specialty and multidisciplinary care annually, and an 11% appointment no-show rate. Among adults, the most frequent diagnoses were abdominal pain (26.3%, 60/228), mechanical back pain (20.2%, 46/228), and H. pylori infection (19.3%, 44/228). Among children, the most frequent diagnoses were upper respiratory tract infection (12.1%, 21/174), Giardia (10.3%, 18/174), and short stature (7.5%, 13/174). CONCLUSIONS: Recently resettled Afghan refugees in Canada were relatively young, experienced diverse health characteristics, and had multi-specialty care engagement in their first two years after arrival. These findings may guide specialized healthcare provision to this inadequately characterized but growing population of refugee arrivals in North America and elsewhere.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".