Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Health-based mobile applications (mHealth) are downloadable applications on a smartphone or similar device for use in health care, either by the person directly or by a health care provider. This Horizon Scan summarizes the available information and provides an overview of health apps on smartphones that are not connected to specialized medical equipment, describing examples of emerging apps in different clinical areas, who they might benefit, and their operational issues. There are over 350,000 mobile applications available for download in app stores, used for a variety of disease areas. These areas include chronic disease, stress, mental health, fitness, sleeping problems, general medication adherence and tracking, and vital sign measurements. The scan identified that health apps often fall into 1 of 4 categories: informational applications, diagnostic applications, disease management applications, and fitness tracking applications. Numerous apps were identified that are available for use by people in Canada. Health apps offer the potential to provide convenience, flexibility, accessibility, and personalized health information. However, the majority of health apps that require evidence of benefit for users have not been assessed in appropriately designed studies that examine their clinical efficacy, or safety. The apps that have been tested in some research studies may have numerous shortcomings in areas such as app design, user engagement, user satisfaction, and retention. This scan describes some operational considerations for apps that relate to their lack of evidence-base, concerns about biases in app design, and the need for equity focused app development. It was noted in the literature that many apps do not provide appropriate privacy and confidentiality for consumers, which may put people at risk of data breaches or inappropriate use of personal data.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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 it