Introduction to mHealth—focused issue on evidence-based eHealth adoption and application
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.
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 | 0.023 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.020 | 0.028 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".