The Financial Burden of Out-of-Pocket Expenses in the US and Canada: How Different is the US?
Bibliographic record
Abstract
Background: This paper compares the burden medical cost-sharing requirements place on households in the US and Canada. It estimates and the probability that individuals with similar demographic features in the two countries have large medical expenses relative to income. Method: We use 2010 nationally-representative household survey data harmonized for crossnational comparisons to identify individuals with high medical expenses relative to income. Using logistic regression, we estimate the probability of high expenses occurring among ten different demographic groups in the two countries. Results: The results show the risk of large medical expenses in the US is one and a half to four times higher than it is in Canada, depending on the demographic group and spending threshold used. The US compares least favorably when evaluating poorer citizens, and when a higher spending threshold is used. Conclusions: Recent health care reforms can be expected to reduce Americans’ catastrophic health expenses, but it will take very large reductions in out-of-pocket expenditures—larger than can be expected—if poorer and middle class families are to have the financial protection from high health care costs that their counterparts in Canada have.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".