Comparing Health of People with Heart Disease in the United States and Canada
Bibliographic record
Abstract
Background: Heart disease is among the leading causes of death in the U.S. and Canada. Despite the U.S.'s higher spending on health care, it is unclear whether persons with heart disease fare better in one country or the other.Methods: To evaluate and compare the health of people aged 45 and older in the U.S. and Canada, we drew upon the Joint Canada-U.S. Survey of Health (JCUSH), a random telephone interview conducted from 2002 to 2003. We used self-reported fair or poor health, disability, and functional impairment as dependent variables in logistic regressions, which controlled for demographic variables and other risk factors.Results: Adjusting for covariates, Canadian respondents with heart disease reported better health as measured by disability, but there was no difference for functional impairment or self-reported fair or poor health. The odds ratios (Canada:U.S.) were 1.10 (p=0.69) for fair or poor health, 0.56 (p=0.06) for disability, and 0.78 (p=0.32) for functional impairment.Conclusions: Our results indicate that people with heart disease are in better health in Canada as measured by disability, but there is no difference for overall self-reported health or functional impairment. Further research must be done to determine the cause of outcomes differences among heart disease patients.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".