Bibliographic record
Abstract
OBJECTIVE: The authors conducted a review of the literature on e-mental health, including its applications, strengths, limitations, and evidence base. METHODS: The rapid review approach, an emerging type of knowledge synthesis, was used in response to a request for information from policy makers. MEDLINE was searched from 2005 to 2010 by using relevant terms. The search was supplemented with a general Internet search and a search focused on key authors. RESULTS: A total of 115 documents were reviewed: 94% were peer-reviewed articles, and 51% described primary research. Most of the research (76%) originated in the United States, Australia, or the Netherlands. The review identified e-mental health applications addressing four areas of mental health service delivery: information provision; screening, assessment, and monitoring; intervention; and social support. Currently, applications are most frequently aimed at adults with depression or anxiety disorders. Some interventions have demonstrated effectiveness in early trials. Many believe that e-mental health has enormous potential to address the gap between the identified need for services and the limited capacity and resources to provide conventional treatment. Strengths of e-mental health initiatives noted in the literature include improved accessibility, reduced costs (although start-up and research and development costs are necessary), flexibility in terms of standardization and personalization, interactivity, and consumer engagement. CONCLUSIONS: E-mental health applications are proliferating and hold promise to expand access to care. Further discussion and research are needed on how to effectively incorporate e-mental health into service systems and to apply it to diverse populations.
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 | 0.039 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.043 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".