Patterns of cost-related medication underuse among Canadian adults with cancer: a cross-sectional study using survey data
Bibliographic record
Abstract
BACKGROUND: Cost-related medication underuse (CRMU) has been reported within the general population in Canada. In this study, we assessed patterns of CRMU among Canadian adults with cancer. METHODS: This is a cross-sectional study using survey data. We accessed data sets from the 2015/16 Canadian Community Health Survey (CCHS) and reviewed the records of adults (≥ 18 yr) with a history of cancer who were prescribed medication in the previous 12 months. We collected information about sociodemographic features, health behaviours and CRMU, and conducted a multivariable logistic regression analysis for factors associated with CRMU. RESULTS: A total of 8581 participants were eligible for the current study. In the weighted multivariable logistic regression analysis, the following factors were associated with CRMU: younger age (odds ratio [OR] 2.55, 95% confidence interval [CI] 1.79-3.63), female sex (male sex v. female sex OR 0.62, 95% CI 0.44-0.88), Indigenous racial background (Indigenous v. White OR 2.37, 95% CI 1.49- 3.77), unmarried status (OR 1.59, 95% CI 1.09-2.30), poor self-perceived health (excellent v. poor self-perceived health OR 0.36, 95% CI 0.17-0.77), lower annual income (< $20 000 v. income ≥ $80 000 OR 3.08, 95% CI 1.75-5.41) and lack of insurance for prescription medications (OR 2.49, 95% CI 1.77-3.50). INTERPRETATION: The toll of CRMU among adults seems to be unequally carried by women, racial minorities, and younger (< 65 yr) and uninsured patients with cancer. Discussion about a national pharmacare program for people without private insurance is needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".