Cholesterol testing among men and women with disability: the role of morbidity
Bibliographic record
Abstract
PURPOSE: Despite more frequent use of health services by people living with disability, the quality of preventive care received may be suboptimal. In this retrospective cohort study, we used administrative data to examine the relationship between cholesterol testing and levels of disability and morbidity among women and men in Ontario, Canada. METHODS: We linked multiple provincial-level databases in this study. In stratified analyses for women and men, we used multivariable logistic regression to examine differences in cholesterol testing, and we tested for an interaction effect between disability and morbidity. In a secondary analysis, we tested for a three-way interaction between sex, disability, and morbidity on the entire cohort. RESULTS: There was an interaction between morbidity and disability for both women and men. Women and men with no chronic conditions appeared to be least likely to be up-to-date on cholesterol testing, and among this group, those with moderate disability were more likely to be up-to-date on cholesterol testing than those with no disability (adjusted odds ratio [AOR] =1.51; 95% confidence interval [CI] 1.20-1.90 for women; AOR =1.16; 95% CI 1.00-1.34 for men). Among women and men who had one chronic condition, having severe disability put them at significant disadvantage versus those with no disability. Only 58.5% of men with no disability and no chronic conditions were up-to-date on cholesterol testing. CONCLUSION: An intermediate level of health care need (reflected in this study as level of disability and level of morbidity) may provide a benefit for cholesterol testing, and conversely, health care needs that are too few or too great may negatively affect testing. Public health and practice-based interventions need to be explored to address these findings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".