A pilot study on cost-related medication nonadherence in Ontario.
Bibliographic record
Abstract
BACKGROUND: Cost-related nonadherence (CRN) describes patients cutting back on their prescribed medication due to an inability to pay. CRN is influenced by drug insurance coverage plans, which vary widely among different healthcare systems. Little is known about CRN in Canada and Ontario. OBJECTIVE: To develop and pilot a questionnaire about CRN. METHODS: An interviewer-administered questionnaire assessing demographics, socioeconomic status, health status and health literacy, medication costs and CRN was developed for this pilot study. Participants were recruited from a general internal medicine rapid assessment outpatient clinic of a large urban teaching hospital. RESULTS: Sixty patients were recruited (mean age 60.3 years; 48.3% female; mean of 5.3 prescription medications per patient). Nine patients (15%) reported some form of CRN. Unfilled prescriptions, delayed prescriptions, less frequent and smaller doses were the most common forms of CRN. Seven patients (11.7%) had no drug insurance. Patients without drug insurance were more likely to experience CRN than patients with private insurance (OR 20.70, 95% CI 1.46-292.75); government coverage also increased the likelihood of CRN compared to private coverage (OR 4.51, 95% CI 0.376-54.11). Patients spending over $100 a month out-of-pocket were more likely to experience CRN than patients spending less than $20 (OR 42.52, 95% CI 2.02-894.03). Thirty-three patients (55%) said that their physicians had not asked them about how they deal with the cost of prescriptions. CONCLUSION: Based on our pilot survey, a significant minority of specialty clinic outpatients experience CRN and prescribers frequently forget to inquire whether patients can afford their medications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".