Usability Assessment of a Mobile Application: Experience and Effects among Family Medicine Residents
Why this work is in the frame
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Bibliographic record
Abstract
This study aimed to identify factors affecting the usage of the IAM mobile application, and to provide a better understanding of residents' needs and experiences as they prepared for their board examination.Twenty family medicine residents at McGill University received the IAM App for their smartphone, loaded with the 99 Priority Topics deemed essential by the College of Family Physicians of Canada to the development of competence in family medicine.One alert to a priority topic was delivered via weekly push notification.The App's usability and residents' experiences were assessed via interview guided by log data on their usage of the App.Fifteen interviews were analyzed.Residents considered the IAM App as a valuable tool for spacing out their learning, and the majority described it as "intuitive" and "easily accessible".Three usage patterns were identified among the residents: continuers, discontinuers and non-users.Cross-case analysis revealed 5 themes: factors that influenced App use, the App's role, motivation for App use, use preference and the App's acceptability.Individual needs, learning strategies and push notifications were the factors that influenced the use of the App.However, proximity to exam dates sustained the use of the App.Barriers to use of the App included technical issues and lack of technical support.The IAM app supports traditional preparation and different learning approaches while promising to foster reflection.Further studies are needed to identify other factors that influence App use and clearer the role of mobile apps to prepare for the board examination.May 8 -13, 2015 Conducted the last open-ended face-to-face interviews and transcription.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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