Is Telepsychiatry Equivalent to Face-to-Face Psychiatry? Results From a Randomized Controlled Equivalence Trial
Bibliographic record
Abstract
OBJECTIVE: The use of interactive videoconferencing to provide psychiatric services to geographically remote regions, often referred to as telepsychiatry, has gained wide acceptance. However, it is not known whether clinical outcomes of telepsychiatry are as good as those achieved through face-to-face contact. This study compared a variety of clinical outcomes after psychiatric consultation and, where needed, brief follow-up for outpatients referred to a psychiatric clinic in Canada who were randomly assigned to be examined face to face or by telepsychiatry. METHODS: A total of 495 patients in Ontario, Canada, referred by their family physician for psychiatric consultation were randomly assigned to be examined face to face (N=254) or by telepsychiatry (N=241). The treating psychiatrists had the option of providing monthly follow-up appointments for up to four months. The study tested the equivalence of the two forms of service delivery on a variety of outcome measures. RESULTS: Psychiatric consultation and follow-up delivered by telepsychiatry produced clinical outcomes that were equivalent to those achieved when the service was provided face to face. Patients in the two groups expressed similar levels of satisfaction with service. An analysis limited to the cost of providing the clinical service indicated that telepsychiatry was at least 10% less expensive per patient than service provided face to face. CONCLUSIONS: Psychiatric consultation and short-term follow-up can be as effective when delivered by telepsychiatry as when provided face to face. These findings do not necessarily mean that other types of mental health services, for example, various types of psychotherapy, are as effective when provided by telepsychiatry.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".