Deprescribing Guidelines: Value of an Interactive Mobile Application
Bibliographic record
Abstract
INTRODUCTION: We developed a new channel on a mobile app as a continuing education tool to augment the use of deprescribing guideline content in clinical practice. In this research brief, we describe the reach and adoption of channel content, as well as user feedback. METHODS: Using Google Analytics, we counted page views of the website (deprescribing.org) where the app was promoted. We calculated total app downloads, monthly active users, and guideline-specific page views. Users were invited to complete the embedded Information Assessment Method (IAM) Questionnaire to obtain feedback on the value of information presented on the Deprescribing Channel. RESULTS: Between March 2, 2019 and November 30, 2019, we documented 9,454 page views of the promotional web page across 40 countries. The Deprescribing Channel was downloaded 3,256 times with an average of 464 monthly users. In total, the guidelines on this channel were accessed 14,377 times with 49,721 views across all guideline pages. Thirty-seven IAM questionnaires were completed. Thirty-two responses indicated this deprescribing information was relevant for at least one of their patients. Regarding educational outcomes, 22 responses were of learning something new and/or being motivated to learn more. CONCLUSION: We documented international interest in a mobile app providing continuing education on deprescribing. App users generated sustained page views over the study period. Feedback from a small number of users was positive with the majority finding the content relevant, educational, and applicable to patient care. Further work is needed to improve the usability of the embedded feedback questionnaire and to evaluate its value in supporting learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".