Physician Visits, Hospitalizations, and Socioeconomic Status: Ambulatory Care Sensitive Conditions in a Canadian Setting
Bibliographic record
Abstract
OBJECTIVE: To determine whether rates of physician visits for ambulatory care sensitive (ACS) conditions are lower for people of low-socioeconomic status than of high-socioeconomic status in an urban population with universal health care coverage. DATA SOURCES/STUDY SETTING: Physician claims and hospital discharge abstracts from fiscal years 1998 to 2001 for urban residents of Manitoba, Canada. The 1996 Canadian Census public use database provided neighborhood household income information. The study included all continuously enrolled urban residents in the Manitoba Health Services Insurance Plan. STUDY DESIGN: Twelve ACS conditions definable using 3-digit ICD-9-CM codes permitted cross-sectional and longitudinal comparison of ambulatory visits and hospitalizations. Neighborhood household income data provided a measure of socioeconomic status. DATA COLLECTION/EXTRACTION METHODS: Files were extracted from administrative data housed at the Manitoba Centre for Health Policy. PRINCIPAL FINDINGS: All conditions showed a socioeconomic gradient with residents of the lowest income neighborhoods having both more visits and more hospitalizations than their counterparts in higher income areas. Six of nine conditions with a sufficient N showed individuals living in the lowest income neighborhoods to have significantly more ambulatory visits before hospitalization for an ACS condition than did those in the most affluent neighborhoods. Many conditions showed a gradient in rate of hospitalization even after controlling for the number of ambulatory care visits. CONCLUSIONS: In the Canadian universal health care plan, the poor have reasonable access to ambulatory care for ACS conditions. Ambulatory care may be more effective in preventing hospitalizations among relatively affluent individuals than among the less well off.
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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 | 0.006 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".