Use of the emergency department by refugees under the Interim Federal Health Program: A health records review
Bibliographic record
Abstract
INTRODUCTION: In June 2012, the federal government made cuts to the Interim Federal Health (IFH) Program that reduced or eliminated health insurance for refugee claimants in Canada. The purpose of this study was to examine the effect of the cuts on emergency department (ED) use among patients claiming IFH benefits. METHODS: We conducted a health records review at two tertiary care EDs in Ottawa. We reviewed all ED visits where an IFH claim was made at triage, for 18 months before and 18 months after the changes to the program on June 30, 2012 (2011-2013). Claims made before and after the cuts were compared in terms of basic demographics, chief presenting complaints, acuity, diagnosis, presence of primary care, and financial status of the claim. Bivariate or multivariate logistic regression analysis was performed to yield odds ratios (OR) with 95% confidence intervals. RESULTS: There were a total of 612 IFH claims made in the ED from 2011-2013. The demographic characteristics, acuity of presentation and discharge diagnoses were similar during both the before and after periods. Overall, 28.6% fewer claims were made under the IFH program after the cuts. Of the claims made, significantly more were rejected after the cuts than before (13.7% after vs. 3.9% before, adjusted OR 4.28, 95% CI: 2.18-8.40; p<0.05). The majority (75.0%) of rejected claims have not been paid by patients. Fewer patients after the cuts indicated that they had a family physician (20.4% after vs. 30% before, unadjusted OR 1.67, 95% CI: 1.14-2.44; p<0.05) yet a higher proportion of patients without a family physician were still advised to follow up with their family doctor during the after period (67.2% after vs. 41.8% before, unadjusted OR 2.85, 95% CI: 1.45-5.62; p<0.05). CONCLUSION: A higher proportion of both rejected and subsequently unpaid claims after the IFH cuts in June 2012, as demonstrated in the logistic regression analysis in this health records review, represents a potential barrier to emergency medical care, as well as a new financial burden to be shouldered by patients and hospitals. A reduction in IFH claims in the ED and a reduction in the number of patients with access to a family physician also suggests inadequate primary care for this population, yet this was not reflected in the follow-up advice offered by ED physicians to patients.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".