Accuracy of Self‐Reported Health Care Use in a Population‐Based Sample of Homeless Adults
Bibliographic record
Abstract
OBJECTIVE: To assess the accuracy of self-reported ambulatory care visits, emergency department (ED) encounters, and overnight hospitalizations in a population-based sample of homeless adults. DATA SOURCE: Self-report survey data and administrative health care utilization databases. STUDY DESIGN: Self-reported health care use in the past 12 months was compared to administrative encounter records among 1,163 homeless adults recruited in 2004-2005 from shelters and meal programs in Toronto, Ontario. DATA EXTRACTION METHODS: Self-reported health care use was assessed using a structured face-to-face survey. Each participant was linked to administrative databases using a unique personal health number or their first name, last name, sex, and date of birth. PRINCIPAL FINDINGS: The sensitivity of self-report for ambulatory care visits, ED encounters, and overnight hospitalizations was 89, 80, and 73 percent, respectively; specificity was 37, 83, and 91 percent. The mean difference between self-reported and documented number of encounters in the past 12 months was +1.6 for ambulatory care visits (95 percent CI = 0.4, 2.8), -0.6 for ED encounters (95 percent CI = -0.8, -0.4), and 0.0 for hospitalizations (95 percent CI = 0.0, 0.1). CONCLUSIONS: Adults experiencing homelessness are quite accurate reporters of their use of health care, especially for ED encounters and hospitalizations.
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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 | 0.003 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".