Bibliographic record
Abstract
Abstract: Providing psychiatric services by real-time videoconferencing has been increasingly adopted as a method of reaching hard-to-serve populations since the early 1990s. As the field has expanded, a growing body of research has developed investigating both how telepsychiatry compares to in-person psychiatric care and how effectively telepsychiatry can be implemented in routine clinical care. A narrative review was performed to consider the evidence that telepsychiatry is feasible and effective across a variety of patient populations and clinical settings. There is a growing body of evidence investigating the efficacy of telepsychiatry when used for psychiatric assessment and treatment in the adult, child, and geriatric populations. Though studies vary in quality, they generally demonstrate that telepsychiatry is effective across multiple age groups and clinical settings. Telepsychiatry is generally well accepted by patients and clinicians and is feasible to implement, with the suggestion that some patients may actually prefer telepsychiatry to in-person treatment. Issues to consider in the implementation of telepsychiatry services include funding and reimbursement, medico-legal issues when provision crosses legislative boundaries, incorporation into existing health systems, and crosscultural considerations. Future directions for research and practice include a need for higher-quality efficacy studies, consideration of data security, increased attention to low- and middle-income countries, and the introduction of novel technological approaches. Keywords: efficacy, service delivery, telemental health, videoconferencing
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 | 0.052 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".