Clinical and Educational Telepsychiatry Applications: A Review
Bibliographic record
Abstract
OBJECTIVE: Telepsychiatry in the form of videoconferencing brings enormous opportunities for clinical care, education, research, and administration. Focusing on videoconferencing, we reviewed the telepsychiatry literature and compared telepsychiatry with services delivered in person or through other technologies. METHODS: We conducted a comprehensive review of telepsychiatry literature from January 1, 1965, to July 31, 2003, using the terms telepsychiatry, telemedicine, videoconferencing, effectiveness, efficacy, access, outcomes, satisfaction, quality of care, education, empowerment, and costs. We selected studies for review if they discussed videoconferencing for clinical and educational applications. RESULTS: Telepsychiatry is successfully used for various clinical services and educational initiatives. Telepsychiatry is feasible, increases access to care, enables specialty consultation, yields positive outcomes, allows reliable evaluation, has few negative aspects in terms of communication, generally satisfies patients and providers, facilitates education, and empowers parties using it. Data are limited with regard to clinical outcomes and cost-effectiveness. CONCLUSIONS: Telepsychiatry is effective. More short- and long-term quantitative and qualitative research is warranted on clinical outcomes, predictors of satisfaction, costs, and educational outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".