Non-Adherence to Prescribed Therapies: Pharmacare’s Existential Challenge
Bibliographic record
Abstract
Pharmacare, a recently proposed addition to Canada's universal medicare program, has become a prominent topic in the public discourse, but funding and leadership have not been established. Repeated Health Care in Canada (HCIC) surveys of the adult public and a broad spectrum of health professionals reveal very strong support for a national system that is easy to access and covers all prescribed pharmaceuticals. Although the practical details of universal pharmacare remain to be established, there is strong support among the public and professionals as well as increasing federal government interest in moving forward and ultimately implementing pharmacare. At the same time, HCIC surveys indicate that a high percentage of patients do not take their medications as directed, both for acute and chronic illnesses. The data suggest that pharmacare's success will be severely challenged by this. Of the four major challenges preventing usual care from being the best care - suboptimal access, non-diagnosis, non-prescription and non-adherence - risk from some form of non-adherence is often ranked first by care professionals. The most commonly reported reasons for non-adherence in clinical settings are patients' forgetfulness and how they feel in the moment on any given day. Costs of therapy, lack of understanding or poor knowledge transfer between prescribers and patients regarding therapeutic risks and benefits are rarely cited causes for poor adherence. These findings from the 2018 HCIC survey are not new. They are very consistent with measurements in the 2016 and other previous HCIC surveys. They do, however, raise practical challenges for the creation and ongoing management of universal pharmacare. Specifically, a patient-centred care component designed to improve non-adherence to prescribed therapies is needed. Ideally, it should include a measurement and feedback component on adherence that shares data with and between patients, health professionals and payers. Things can be better.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".