Why does increasing public access to medicines differ between countries? Qualitative comparison of nine countries
Bibliographic record
Abstract
OBJECTIVE: To identify factors associated with differences between developed countries in reclassifying (switching) medicines from prescription to non-prescription availability. METHODS: Cross-national qualitative research using a heuristic approach in the US, UK, Japan, Australia and New Zealand, supplemented by data from Canada, Denmark, the Netherlands and Singapore. In-depth interviews with 80 key informants (65 interviews) explored and compared factors in terms of barriers and enablers to reclassification of medicines in each country. Document analysis supplemented interview data. RESULTS: Each country had a unique mix of enablers and barriers to reclassification. Enablers included government policy (particularly in UK), pharmacist-only scheduling (particularly in Australia and New Zealand) and large market size (particularly in the US and Europe). Local barriers included limited market potential in small countries, the cost of a reclassification (particularly in the US), competition from distributors of generic medicines, committee inconsistency and consumer behavior. UK had more enablers than barriers, whereas in Australia the opposite was true. CONCLUSIONS: Different factors limit or enable reclassification, affecting consumer access to medicines in different countries. For countries attempting to reduce barriers to reclassification, solutions may include garnering government support for reclassification, support and flexibility from the medicines regulator, having a pharmacy-only and/or pharmacist-only category, providing market exclusivity, ensuring best practice in pharmacy, and minimizing the cost and delays of reclassification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".