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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".