Bibliographic record
Abstract
Background Do we need to work from offices in psychiatry? The clinical interface has been debated particularly in child and adolescent psychiatry with continued beliefs related to the differences in therapeutic alliance when compared to face-to-face practice. That literature clearly shows that telepsychiatry is equal in its therapeutic effects. But not much has been written about the other advantages of telepsychiatry, which may be intuitive but needs to be documented. Methodology The University of Toronto Telepsychiatry Program is the largest in the world with over 60 psychiatrists and 1400 sites. This is an anaectodal review of 25 years of practice using this medium outlining the advantages (ADV) and disadvantages (DADV) to this medium. Results ADV: convenience from home, complete access to hospital files, physician safety during sessions, able to see multiple sites and include multisystem professionals including schools, cost effective (when compared to outreach psychiatry), simplicity of connection with minimal interference. DADV: novelty to client, quality of video to pick up very subtle nonverbal information, technical support required, capital cost to set up, mental health biases to technology. Conclusion This technology is evolving. It is essential physicians understand the issues whether it be privacy, cost, utility and clinical application. The long-term impact will likely affect future practice and allow resource sensitive care to outlying areas with the ability to impact a country's mental health significantly. Health economic data is required for future research. Disclosure of interest The author has not supplied his declaration of competing interest.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| 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".