Acute primary health care needs of Syrian refugees immediately after arrival to Canada
Bibliographic record
Abstract
OBJECTIVE: To describe the population of Syrian refugees who received care at temporary triage clinics, the health issues addressed, and the health services used in the clinics. DESIGN: Cross-sectional retrospective study using electronic extraction of medical data of refugees attending the primary care triage clinics. SETTING: Temporary triage clinics in Ottawa, Ont. PARTICIPANTS: Newly arrived Syrian refugees temporarily housed in hotels in Ottawa in 2016. MAIN OUTCOME MEASURES: Demographic characteristics (age and sex), number of clinic visits per patient, diagnostic assessments (categorized using the ENCODE-FM [Electronic Nomenclature and Classification of Disorders and Encounters for Family Medicine] at each patient visit [≥ 1 diagnostic assessment could be entered per visit]), laboratory tests requested per visit, and medications prescribed per visit. RESULTS: Of the 912 newly arrived Syrian refugees, 338 (37.1%) visited the clinics, resulting in 822 diagnostic assessments (154 different types of ENCODE-FM diagnostic assessments). Refugees’ age ranged from 1 month to 62 years, with a median age of 13.5 years. Among the refugees, 50.9% were female and 1 patient’s sex was not documented in the electronic medical record. The number of visits to the clinic per patient varied from 1 to 7. Most frequent diagnoses were as follows: acute upper respiratory infection (24.0%), social-cultural problems (11.2%), and pharyngitis (8.6%). Most frequent diagnoses for multiple clinic visits included hypertension, acute upper respiratory infection, and child developmental problems. Adults were prescribed significantly more medications (P = .036) and received significantly more laboratory test requisitions (P = .006) compared with children. CONCLUSION: Primary care triage clinics on-site where newly arrived Syrian refugees were housed provided basic care for simple ambulatory conditions and might have prevented overloading of other components of the health care system.
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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 | 0.000 | 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".