Opportunities for improving how and when Canadians are informed about new prescription medications
Bibliographic record
Abstract
The Canadian prescription process requires a person to go through several steps. Prescription medications have associated risks and benefits and it is important for people to be aware of these before and while they are taking medications. One of the approaches to informing people about new prescription medications is that they are provided Consumer Medication Information (CMI). CMI is given to Canadians at the pharmacy when they pick up prescriptions, they will be taking for the first time. This study used semi-structured interviews to examine the lived experiences of a sample of Canadians (N = 36) to identify opportunities for improvement in how and when they are informed about new prescription medications. The findings were synthesized into a journey map. Generally, participants wanted to receive CMI digitally and earlier in the prescription process. Adopting these changes could have several benefits which include loss prevention and increased accessibility to CMI as well as more participatory decision making and opportunities to ask questions. Future research is warranted to explore similar topics with a larger sample and determine what method (e.g., email, website, mobile application) would be most suitable.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".