Limitations in the routine use of telepsychiatry
Bibliographic record
Abstract
Telepsychiatry is well established in many countries, but there is still little information about its use in routine health care. We reviewed the literature for information on the use of telepsychiatry in mental health services. From 1033 publications identified in the literature search and through references from a separate project, 16 studies or descriptions of the routine use of telepsychiatry services were selected for further review. Eleven of these articles dealt primarily with videoconferencing and five with telephone- based services. Clinical use of videoconferencing in the programmes described by the reviewed papers was modest, with an average of 16 consultations per month. Three of the telephone-based services had large numbers of clients. The papers we reviewed gave limited consideration to the healthcare systems in which telepsychiatry was provided and to the use of conventional mental health services. Telepsychiatry appears to still be a niche technology in many health systems. A lack of champions for the technology and reimbursement problems may contribute to the limited use of this area of telemedicine.
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 | 0.075 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".