Do Weekly Alerts From a Mobile Application Influence Reading During Residency?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: The benefits of “spaced education” have been documented for residents in highly focused specialties. We found no published studies of spaced education in family medicine. In this study, we report on the feasibility of delivering weekly alerts from a mobile application (app) developed for exam preparation, to increase the reading of clinical information in the family medicine residency. Methods Design: This is a 2-phase mixed methods study. Phase one is a quasi-experimental study of resident reading of information related to priority topics in family medicine. Reading was documented by page views in a noncommercial mobile app. Participants: All incoming first-year residents at two university training programs in Canada. The intervention group received one alert per week to priority topics on the app, beginning in their second month of residency. The control group was given access to the same app, but received no alerts. Results: In this paper, we report the phase one preliminary findings. In the intervention group, 81 of 96 first year residents consented. At the control site, 79 of 85 residents consented. After 100 days, intervention group residents had viewed more pages of clinical information across all 99 priority topics (1,546 versus 900) and per topic (15.7 versus 9.1 pages, P < 0.0003). On average, each increase of one visit to the app following a weekly alert was associated with an increase of 3.2 visits to pages of clinical information in the app. Conclusion: A weekly alert delivered via mobile app shows promise with respect to reading in the family medicine residency.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it