Analysis of Telehealth Versus Telemedicine Terminology in the <i>Telemedicine and e-Health</i> Journal Between 2010 and 2020
Bibliographic record
Abstract
Introduction: The terms “ telemedicine ” and “ telehealth ” are similar, yet, carry different meanings and are often defined differently. Methods: A decadal longitudinal study analyzing the usage of these terms in the Telemedicine and e-Health Journal (TMJ) between 2010 and 2020 was conducted. Looking at the keywords assigned to the “ Original Research ” articles, “ telemedicine ” (34%) is used almost three times more than telehealth (12%). Although “ other ” keywords are assigned at a similar frequency as “ telemedicine ,” a similar pattern is followed for the terms within the text. Results: “ Telemedicine ” and “ other ” terms are used the most (36%), while articles using both (“ telemedicine ” and “ telehealth ”) (15%) or “ telehealth ” (14%) as terms throughout the article are less. This longitudinal study also analyzed the TMJs editorial board between 2010 and 2020. Most of the board is made up of physicians (MD or DO) (42) or PhDs (33), with 25 out the 75 having dual credentialing. Conclusions: Also, while there is international influence within the board (UK, India, France, Canada, etc.), most of the board is associated with an American organization (educational and/or corporate). Most of the board (34/75) has also been present between 10 and 11 years within the study period (a total of 11 years).
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 | 0.011 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".