Striving for evidence in e-health evaluation: Lessons from health technology assessment
Bibliographic record
Abstract
Evaluation is crucial to the integration of e-health applications into the health-care system and their ultimate sustainability. However, e-health evaluation is often criticized for the poor quality of research design, the lack of common outcome indicators and the absence of an agreed theory. Health technology assessment (HTA) could offer a sound methodological basis for e-health evaluation. However, there have been major concerns about the applicability of the HTA approach to the evaluation of e-health initiatives. Evaluators -- and decision makers -- must accept that telehealth evaluation may serve different purposes for different stakeholders, and therefore concede that no single evaluation framework or methodology, even the randomized controlled trial, is totally objective. To address the complex environment of telehealth evaluation, a participatory strategy is useful, whereby stakeholders are involved in the study design and definition of evaluation questions at each phase. This will also build confidence between the evaluation team and the stakeholders, facilitating informed decision making through an integrated knowledge mobilization activity.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".