Bibliographic record
Abstract
Smartphones have a variety of unique features including text-message communication, camera, sensors, and health applications (apps), which can be used to assist in monitoring an individual’s health, diet, and exercise, as well as support goal-focused strategies personalized to user needs. Mental health and diabetes management apps are two prominent examples that have been shown to be effective in improving specific health outcomes. Mental health apps provide day-to-day patient care by teaching users how to reduce stress, focusing on strategies to enhance mental well-being. Apps such as Kokoro, Headspace, and PRISM have been demonstrated to reduce symptoms of depression and anxiety, and psycho-education apps have been demonstrated to reduce symptoms and to enhance concentration during specific tasks. Many diabetes apps are accessible by patients and physicians, and include tracking features for nutrition, fitness, and hemoglobin A1c levels. Specialized apps with text-messaging services and personalized support have been associated with improvements in blood pressure and blood glucose control. Social forums also provide patients privacy and the freedom to discuss their conditions with comfort. Health apps are easily accessible and available at low or no cost, and can be an effective tool for educating patients with chronic disease, supporting collaborative self-management, extending the impact of healthcare providers, and include response anonymity. There remain significant challenges including the protection of private health information and the development of regulatory frameworks to evaluate app quality, effectiveness, and absence of harm. Overall, the implementation of smartphone apps in healthcare systems may decrease demand in clinics, reduce healthcare costs, and lead to an improvement in patient health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".