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 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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".