Social Class and Hospitalization in Canada
Bibliographic record
Abstract
IntroductionDespite the existence of a universal health care system in Canada, there remains an inverse relationship between social class and health (Frohlich 2006). Those who identify as lower social class (operationalized with various indicators, including education, income, and occupation) have poorer outcomes across multiple health measures (Tang 2016).
 Objectives and ApproachThis study examines the link between social class and health care utilization, specifically hospitalization, in Canada. First, we examine the relationship between different indicators of social class and rates of hospitalization; next, we look at cause-specific hospitalizations. Using the unique dataset that contains the linked data for the 2006 Census with the Discharge Abstract Database for 2006-9, we explore the following research questions:
 
 Are the three main indicators of social class, education, income, and occupation, individually correlated with hospitalization rates overall, controlling for age and gender?
 Are certain indicators of social class more highly correlated with hospitalization rates, controlling for other indicators?
 
 ResultsWe access the linked files provided by Statistics Canada in the Prairie Research Data Centre. The long-form Census represents approximately 20\% of the Canadian population. The DAD includes data on hospitalizations in acute care facilities in Canada, with the exception of those in the province of Quebec. Approximately 4,650,000 long-form respondents were eligible for linkage to the DAD, and approximately 5.3\% of Census respondents were linked to at least one DAD record between 2006 and 2009. Our analyses are ongoing, but initial results suggest an inverse relationship between hospitalization and various measures of social class. Full results will be made available for presentation following vetting by Statistics Canada personnel.
 Conclusion/ImplicationsThis data provides us with a unique opportunity to examine the relationship between the detailed and rich measures of social class collected in the long-form Census and the comprehensive hospitalization data provided by the DAD records. Results will have implications for hospital health care provision across Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".