The Scope of Mobile Apps in Health Domain: Highlighting Applications Recommended by Known Organizations
Bibliographic record
Abstract
Background and objectives : With over one billion smartphones and 100 million tablets across the world, they can perform as a valuable tool in health care management and transform health care through using mobile applications. There are various medical apps in different areas for a variety of users and among these applications, some of them have been approved and recommended by well-known organizations. Material and Methods : This is a descriptive-comparative study. This article reviews the mobile apps in health and medical domains developed or recommended by BMJ, NHS, CDC, AMA, Georgetown University, NLM, University of Ottawa, HHS, UCSF, and NYC. Results : The results of this research are presented in terms of the organization developing or recommending the app, the applications’ scope, the usage category of the application and the apps’ users. Conclusion : It seems that the organizations envisioning transformation of their services are the ones which recognize the impact of mobile technologies in this regard, particularly mobile apps.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".