Do Weekly Alerts From a Mobile Application Influence Reading During Residency?
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.
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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 | 0.003 | 0.037 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".