Cost-related nonadherence to prescription medications in Canada: a scoping review
Bibliographic record
Abstract
PURPOSE: The evidence is emerging that prescription medications are the topmost drivers of increasing health care costs in Canada. The financial burden of medications may lead individuals to adopt various rationing or restrictive behaviors, such as cost-related nonadherence (CRNA) to medications. Therefore, the purpose of this study is to provide an overview of the type, extent, and quantity of research available on CRNA to prescription drugs in Canada, and evaluate existing gaps in the literature. METHODS: The study was conducted using a scoping review methodology. Six databases were searched from inception till June 2017. Articles were considered for inclusion if they focused on extent, determinants, and consequences of CRNA to prescription medication use in the Canadian context. Variables extracted for data charting included author(s), year of publication, study design, the focus of the article, sample size, population characteristics, and key outcomes or results. RESULTS: This review found 37 studies that offered evidence on the extent, determinants, and consequences of CRNA to prescription medications in Canada. Depending on the population characteristics and province, the prevalence of CRNA varies between 4% and 36% in Canada. Canadians who are young (between 18 and 64 years), without drug insurance, have lower income or precarious or irregular employment, and high out-of-pocket expenditure on drugs are most likely to face CRNA to their prescriptions. The evidence that CRNA has negative health and social outcomes for patients is insufficient. Literature regarding the influence of prescribing health care professionals on patients' decisions to stop taking medications is limited. There is also a dearth of literature that explores patients' decisions and strategies to manage their prescription cost burden. CONCLUSION: More evidence is required to make a strong case for national Pharmacare which can ensure universal, timely, and burden-free access to prescription medications for all Canadians.
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.041 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".