Socioeconomic Status and Utilization of Health Care Services in Canada and the United States
Bibliographic record
Abstract
OBJECTIVES: Building on Andersen's behavioral model for the utilization of health care services, we examined factors associated with utilization of physician and hospital services among adults in Canada and the United States, with a focus on socioeconomic status (enabling resources in Andersen's framework). METHODS: Using the 2002-2003 Joint Canada/United States Survey of Health, we conducted country-specific multivariate logistic regressions predicting doctor contacts/visits and overnight hospitalizations in the past year, controlling for predisposing characteristics, enabling resources, and several factors representing perceived need for health care. All analyses were appropriately weighted to yield nationally representative results. RESULTS: Several measures of socioeconomic status-having a regular medical doctor, education, and, in the US income and insurance coverage-were associated with doctor contacts or visits in both countries, along with various predisposing and need factors. However, these same measures were not associated with hospitalizations in either country. Instead, only the individual's predisposing characteristics (eg, age and sex) and his/her need for health care predicted utilization of hospital services in Canada and the United States. Insurance coverage status in the United States became a significant predictor of hospitalizations when count data were analyzed via Poisson regression. CONCLUSIONS: Given our particular outcome measures, adults in Canada and the United States exhibited similar patterns of hospital utilization, and socioeconomic status played no explanatory role. However, relative to Canadian adults, we found disparities in doctor contacts among US adults-between those with more income and those with less, between those with health insurance and those without-after adjusting for health care needs and predisposing characteristics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".