The Effectiveness of Telemental Health Applications: A Review
Bibliographic record
Abstract
OBJECTIVE: To review the evidence of benefit from use of telemental health (TMH) in studies that reported clinical or administrative outcomes. METHOD: Relevant publications were identified through computerized literature searches using several electronic databases. Included for review were scientifically valid articles that described controlled studies, comparing TMH with a non-TMH alternative, and uncontrolled studies that had no fewer than 20 participants. Quality of the evidence was assessed with an approach that considers both study performance and study design. Judgments were made on whether further data were needed to establish each TMH application as suitable for routine clinical use. RESULTS: Included in the review were 72 papers that described 65 clinical studies; 32 (49%) studies were of high or good quality. Quality of evidence was higher for Internet- and telephone-based interventions than for video conferencing approaches. There was evidence of success with TMH in the areas of child psychiatry, depression, dementia, schizophrenia, suicide prevention, posttraumatic stress, panic disorders, substance abuse, eating disorders, and smoking prevention. Evidence of success for general TMH programs and in the management of obsessive-compulsive disorder were less convincing. Further study was judged to be necessary or desirable in 53 (82%) of the studies. CONCLUSION: Evidence of benefit from TMH applications is encouraging, though still limited. There is a need for more good-quality studies on the use of TMH in routine care. The emerging use of Internet-based applications is an important development that deserves further evaluation.
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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".