An Econometric Study of the Determinants of Canada’s Non-reimbursable Medical Care Costs
Bibliographic record
Abstract
Introduction Several studies have assessed the linkages between household factors and non-reimbursable medical costs over the years. However, there still exists a substantial gap in information on non-reimbursable medical costs in Canada that requires addressing. For instance, more information is needed about the extent and variation of the non-reimbursable medical costs across Canada. Even less is known about the prevalence of these costs among different population segments. We use the survey of household spending data to predict non-reimbursable medical costs across Canada’s 10 provinces. Methods In order to estimate the predictors of non-reimbursable medical costs in Canada, descriptive assessments and weighted cross-sectional regression analyses were conducted. Regression estimates on the Canadian survey of household spending data were performed to estimate the econometric predictors of non-reimbursable medical costs. Results Findings showed significant variation in non-reimbursable medical costs across the country’s 10 provincial regions. More importantly, they show that the share of earnings spent on non-reimbursable medical services is negatively associated with household earnings itself (estimated, coefficient of ln(Earnings) =-0.73, -0.73, -0.85, ∀ p<5% for 2004, 2009, 2015, respectively), while at the same time increasing with agedness (estimated, coefficient of Canadians aged>65 years = 0.58 & 0.82, ∀ p<5% versus Canadians aged < 19 years, for 2004, 2009, respectively), feminine gender (estimated, coefficient of feminine gender =0.28, 0.22, ∀ p<5% versus masculine gender for 2004, 2009, respectively), married status, living in large-sized families, and ill-health. Conclusion In Canada, non-reimbursable medical costs differ substantially by province and across socioeconomic, demographic, and health dimensions.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".