Introduction to mHealth—focused issue on evidence-based eHealth adoption and application
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
eHeath has played an important role in improving healthcare services in many developing and developed countries at reducing health disparities and improving health equity (1). These solutions have also been used to improve access to sources of knowledge for both patients and healthcare providers. The advancements in Electronic Health Records (EHR), Picture Archiving and Communication Systems (PACS), and Health Management Information System (HMIS) provide support to healthcare professionals and managers for better decision-making. Teleconsultations using live and store-and-forward technologies have improved access of people to specialized healthcare services in almost all the subspecialties (2). The use of Internet and hand-held devices has opened new avenues for health promotion. Most of this use is driven by reduction in Internet charges, high use of mobile phones and PDAs, and lowering of hardware cost (3). These enablers have led to high teledensity and a tremendous increase in connectivity. However, there is a need of highlighting evidence in the following areas:
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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