The Medium Is the Message: How Do Canadian University Students Want Digital Medication Information?
Bibliographic record
Abstract
(1) Background: To facilitate optimal prescription medication benefits and safety, it is important that people are informed about their prescription medications. As we shift towards using the digital medium to communicate medication information, it is important to address the needs and preferences of different user groups so that they are more likely to read and use this information. In this study, we examined what digital medication information (DMI) format Canadian University students want and why. (2) Methods: This study was a qualitative investigation of young (aged 18–35) Canadian University students’ (N = 36) preferences and rationale supporting these preferences with respect to three potential formats for providing DMI: email, a mobile application (app), and online. Reported advantages and disadvantages of each of the three DMI formats were identified and categorized into unique themes. (3) Results: Findings from this study suggest that Canadian University Students most want to receive DMI by email, followed by a mobile app, and finally they were least receptive to online DMI. Participants provided diverse themes of reasons supporting their preferences. (4) Conclusions: Different user groups may have different needs with respect to receiving DMI. The themes from this study suggest that using a formative evaluation framework for assessing different DMI formats may be useful in future research. Email may be the best way to share DMI with younger, generally healthy, Canadian University students who are on few medications. Further research is required to explore whether other mediums for DMI are more appropriate for users with other characteristics (e.g., older and less educated) and contexts (e.g., polypharmacy and complex conditions). Given the flexibility of digital information, DMI could plausibly be provided in multiple formats and could allow users to choose the option they like best and would be most likely to use.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".