Global Outreach of a Locally-Developed Mobile Phone App for Undergraduate Psychiatry Education
Bibliographic record
Abstract
Background Over the past decade, there have been massive developments in both Web-based and mobile phone technologies. Mobile phones are well accepted by students, trainees, and doctors. A review of the current literature has identified the following specialties that have used mobile phones in education: pediatrics, ophthalmology, nephrology, plastic surgery, orthopedics, pharmacology, and urology. However, to date, there are no published papers examining the application of the latest mobile phone technologies for psychiatry education internationally. Objectives The main objectives of this study are (1) to determine the feasibility and receptiveness of a locally-developed psychiatry mobile phone app and user perspectives (both quantitative and qualitative) towards it, and (2) to determine the receptiveness of a locally-developed app for psychiatry education internationally. Methods A Web-based app that contained textbook contents, videos, and quizzes was developed using HTML5 technologies in 2012. Native apps were subsequently developed in 2013. Information about the apps was disseminated locally to Singaporean medical students, but the respective native apps were made available on the app stores. A user perspective survey was conducted locally to determine student’s perception of the app. Results From the inception of the app until the time of preparation of this manuscript, there have been a cumulative total of 28,500 unique visits of the responsive HTML5 Web-based mobile phone app. There have been a cumulative total of 2200 downloads of the Mastering Psychiatry app from the Apple app store and 7000 downloads of the same app from the Android app store. The initial user perspective survey conducted locally highlighted that approximately a total of 95.2% (177/186) of students felt that having a psychiatry mobile phone app was deemed to be useful. Further chi-squared analysis demonstrated that there was a significant difference between males and females in their perception of having textbook contents in the mobile phone app (χ24=12.9, P=.0012). Conclusions To the best of our knowledge, this is the first study to demonstrate the feasibility and global acceptance of a local, self-designed educational app for psychiatry education. Whilst the current research has managed to demonstrate the feasibility and acceptance of such an app, future studies would be warranted to look, in-depth, into whether there are cultural differences in terms of perceptions towards having such an app in psychiatry and what contents different cultures and cohorts of students might want within an app.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".