Data from: National monitoring and evaluation of eHealth: a scoping review
Bibliographic record
Abstract
Objective: There has been substantial growth in eHealth over the past decade, driven by expectations of improved healthcare system performance. Despite substantial eHealth investment, little is known about the monitoring and evaluation strategies for gauging progress in terms of eHealth availability and use. This scoping review aims to map the existing literature and depict the predominant approaches and methodological recommendations to national and regional monitoring and evaluation of eHealth availability and use in order to advance national strategies for monitoring and evaluating eHealth. Methods: Peer-reviewed and grey literature on monitoring and evaluation of eHealth availability and use, published between January 1st, 2009, to March 11th, 2019, were eligible for inclusion. 2,354 publications were identified, and 36 publications were included after full-text review. Data on publication type (e.g. empirical research), country, level (national or regional), publication year, method (e.g. survey), and domain (e.g., Provider-centric electronic record) was charted. Results: The majority of publications monitored availability alone or applied a combination of availability and use measures. Surveys were the most common data collection method (used in 86% of the publications). OECD, European Commission, Canada Health Infoway, and WHO have developed comprehensive eHealth monitoring and evaluation methodology recommendations. Discussion: Establishing continuous national eHealth monitoring and evaluation, based on international approaches and recommendations, could improve the ability for cross-country benchmarking and learning. This scoping review provides an overview of the predominant approaches and recommendations to national and regional monitoring and evaluating eHealth and thereby provides a starting point for developing national eHealth monitoring strategies.
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 | 0.093 | 0.284 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.048 | 0.044 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".