“What Is eHealth”: Time for An Update?
Bibliographic record
Abstract
The annual number of articles reporting on eHealth interventions has increased over the last 10 years. In contrast, the last article in this journal on the definition of eHealth was published in 2006. This leads to the question whether the field itself has reached consensus about the definition and description of eHealth or whether it is in need for a new review of the literature and a new description of the rapidly changing field of eHealth. Since the JMIR community has successfully collaborated on the "CONSORT-eHealth" in the past, we would like to use the same strategy to explore the need for a new definition of eHealth and the creation of a taxonomy for this field. Therefore, we hereby submit a call to all JMIR-readers, to fill out a 4-question survey on their ideas about a refined eHealth definition. Based on these results, we will decide whether or not to engage in a systematic review. Logically, the entire JMIR community is invited to join us in our attempt to further elucidate the field of eHealth.
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 | 0.100 | 0.294 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.017 | 0.052 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".