Application of Low-Cost Methodologies for Mobile Phone App Development
Bibliographic record
Abstract
BACKGROUND: The usage of mobile phones and mobile phone apps in the recent decade has indeed become more prevalent. Previous research has highlighted a method of using just the Internet browser and a text editor to create an app, but this does not eliminate the challenges faced by clinicians. More recently, two methodologies of app development have been shared, but there has not been any disclosures pertaining to the costs involved. In addition, limitations such as the distribution and dissemination of the apps have not been addressed. OBJECTIVE: The aims of this research article are to: (1) highlight a low-cost methodology that clinicians without technical knowledge could use to develop educational apps; (2) clarify the respective costs involved in the process of development; (3) illustrate how limitations pertaining to dissemination could be addressed; and (4) to report initial utilization data of the apps and to share initial users' self-rated perception of the apps. METHODS: In this study, we will present two techniques of how to create a mobile app using two of the well-established online mobile app building websites. The costs of development are specified and the methodology of dissemination of the apps will be shared. The application of the low-cost methodologies in the creation of the "Mastering Psychiatry" app for undergraduates and "Déjà vu" app for postgraduates will be discussed. A questionnaire survey has been administered to undergraduate students collating their perceptions towards the app. RESULTS: For the Mastering Psychiatry app, a cumulative total of 722 users have used the mobile app since inception, based on our analytics. For the Déjà vu app, there has been a cumulative total of 154 downloads since inception. The utilization data demonstrated the receptiveness towards these apps, and this is reinforced by the positive perceptions undergraduate students (n=185) had towards the low-cost self-developed apps. CONCLUSIONS: This is one of the few studies that have demonstrated the low-cost methodologies of app development; as well as student and trainee receptivity toward self-created Web-based mobile phone apps. The results obtained have demonstrated that these Web-based low-cost apps are applicable in the real life, and suggest that the methodologies shared in this research paper might be of benefit for other specialities and disciplines.
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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 | 0.019 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".