Acceptability of a short list of essential medicines to patients and prescribers
Bibliographic record
Abstract
OBJECTIVE: To determine the acceptability of providing free access to only a short list of medicines used in the Carefully seLected and Easily Accessible at No charge Medications (CLEAN Meds) trial. DESIGN: A multimethod explanatory sequential design including interviews with trial participants and focus groups with prescribers. SETTING: Ontario. PARTICIPANTS: Participants in the intervention arm of the CLEAN Meds trial and primary care providers who prescribed medicines to those in the intervention arm of the trial. MAIN OUTCOME MEASURES: The number of trial participants in each prescription category (ie, prescribed no off-list medicine, prescribed 1 off-list medicine, or prescribed 2 or more off-list medicines) and the acceptability of the list to both participants and prescribers. RESULTS: There were 395 participants in the intervention group of the CLEAN Meds trial, but 16 participants withdrew consent or were not prescribed any medicines during the first 12 months of the trial, resulting in a total of 379 participants in the quantitative component of this study. Of the 2648 total prescriptions, 2349 (89%) were for medications that were on or had an equivalent covered by the list. Random sampling was used to select 5 participants to interview from each prescription category. A total of 19 prescribers participated in the focus groups. Themes from participant interviews included the following: having access to medicines on the list was a relief, participants trusted health care professionals to switch medicines and to decide which medicines should be on a publicly funded list, and a short list of essential medicines should be publicly funded. Major themes from the prescribers' focus groups related to the process of developing the list, support for the list, and publicly funding a short list of essential medicines in Canada. CONCLUSION: The consensus among trial participants and prescribers is that the short list of medicines used in the trial is comprehensive and provides access to medicines commonly prescribed.
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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 | 0.025 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".