Hard-to-Reach Populations and Administrative Health Data
Bibliographic record
Abstract
BACKGROUND: Intervention studies with vulnerable groups in the emergency department (ED) suffer from lower quality and an absence of administrative health data. We used administrative health data to identify and describe people experiencing homelessness who access EDs, characterize patterns of ED use relative to the general population, and apply findings to inform the design of a peer support program. METHODS: We conducted a serial cross-sectional study using administrative health data to examine ED use by people experiencing homelessness and nonhomeless individuals in the Niagara region of Ontario, Canada from April 1, 2010 to March 31, 2018. Outcomes included number of visits; unique patients; group proportions of Canadian Triage and Acuity Scale (CTAS) scores; time spent in emergency; and time to see an MD. Descriptive statistics were generated with t tests for point estimates and a Mann-Whitney U test for distributional measures. RESULTS: We included 1,486,699 ED visits. The number of unique people experiencing homelessness ranged from 91 in 2010 to 344 in 2017, trending higher over the study period compared with nonhomeless patients. Rate of visits increased from 1.7 to 2.8 per person. People experiencing homelessness presented later with higher overall acuity compared with the general population. Time in the ED and time to see an MD were greater among people experiencing homelessness. CONCLUSIONS: People experiencing homelessness demonstrate increasing visits, worse health, and longer time in the ED when compared with the general population, which may be a burden on both patients and the health care system.
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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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".