Clinical applications of videoconferencing: a scoping review of the literature for the period 2002–2012
Bibliographic record
Abstract
We conducted a scoping review of the literature on the clinical applications of videoconferencing. Electronic searches were performed using the PubMed, Embase and CINHAL databases to retrieve papers published from 2002 to 2012 that described clinical applications of videoconferencing. The initial search yielded 4923 records and after removing the duplicates and screening at title/abstract level, 505 articles met the inclusion criteria and were reviewed at full-text level. The countries with the highest number of papers were the US, Australia and Canada. Most studies were non-randomised controlled trials. The discipline with highest number of published studies (39%) was mental health, followed by surgery (7%) and general medicine (6%). The type of care delivered via video comprised acute, sub-acute and chronic care, but in 44% of the papers, the intervention was used for a combination of these purposes. Videoconferencing was used for all age groups but more frequently for adults (20%). Most of the papers (91%) reported using videoconferencing for several clinical purposes including management, diagnosis, counselling and monitoring. The review showed that videoconferencing has been used in a wide range of disciplines and settings for different clinical purposes. The practical value of published papers would be improved by following standard guidelines for reporting research projects and clinical trials.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.029 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".