Cross-sectional Study Examining the Differences in the Prevalence of Health Service Deficits among US and Canadian Adults with atleast One of the Chronic Illnesses of COPD, Asthma, Arthritis, and/or Diabetes
Bibliographic record
Abstract
Background: This study compares health service deficits (HSDs) experienced by US adults with chronic illness with their Canadian counterparts. This study was undertaken in order ascertain if there were differences between the two populations given the differences in health care systems. Further, this comparison allows for a partial assessment of the impact the US Affordable Care Act might have on the prevalence of HSDs for US adults with at least one chronic illness (asthma, diabetes, arthritis, COPD). Methods: Bivariate and multivariate techniques were used to analyze US and Canadian health surveillance data in order to compare the prevalence of HSDs and ascertain the characteristics of adults with chronic illness who have HSDs. Results: Multivariate logistic regression analysis using having HSDs as the dependent variable and mutually adjusting for each of the study covariates, yielded that for the study populations non-Caucasians or visible minorities, those under 65 years of age, those with annual household incomes of <$50,000, and those defining their health as fair to poor all had greater odds of having at least one HSD. In difference to the Canadian population, the US population also had greater odds of being male and not being a university graduate. Conclusions: Using Canada as a proxy we were able to compare the prevalence of HSDs between a population with and without universal health care insurance. Our analyses revealed a lower prevalence of HSDs among adult Canadians with at least one chronic illness, suggesting that the 2010 US Affordable Care Act may over time result in a reduction of HSDs in the comparable US population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".