Acceptability and feasibility of a national essential medicines list in Canada: a qualitative study of perceptions of decision-makers and policy stakeholders
Bibliographic record
Abstract
BACKGROUND: Policy approaches have been considered to address inconsistent and inequitable prescription drug coverage in Canada, including a national essential medicines list. We sought to explore key factors influencing the acceptability and feasibility of an essential medicines list in Canada. METHODS: We conducted semi-structured interviews with decision-makers and other key stakeholders from government or pan-Canadian institutions, civil society and the private sector across Canada. We analyzed data using inductive thematic analysis and by applying Kingdon's Multiple Streams Framework to analyze the emergent themes deductively. RESULTS: We conducted 21 interviews before thematic saturation was achieved. We categorized emergent themes to describe the problem, the essential medicines list policy (including content and process), and politics. There was consensus among participants that prescription drug coverage was an important problem to address. Participants differed in their views on how to define essential medicines and concerns about what would be excluded from an essential medicines list. There was consensus on important features for a process to develop an essential medicines list: an independent decision-making body, use of defined selection criteria based on quality evidence, and clear communication of the purpose of the essential medicines list. Federal government financing and the broader pharmacare model, engagement of various interest groups and changing political agendas emerged as core political factors to consider if developing a Canadian essential medicines list. INTERPRETATION: Although stakeholders' views on the content of a Canadian essential medicines list varied, there was consensus on the process to formulate and implement an essential medicines list or common national formulary, including choosing medicines based on best evidence. Greater understanding is now needed on how patients, clinicians and the public perceive the concept of an essential medicines list.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".