Bibliographic record
Abstract
PURPOSE OF REVIEW: The current article defines and surveys E-health: Internet and technology-guided interventions and tools useful for mood disorders. RECENT FINDINGS: E-health encompasses many categories, including computerized self-help strategies, online psychotherapy, websites that provide information, social media approaches including Facebook, Internet forums for health discussions, personal blogs, and videogames. Multiple tools exist to assess and document symptoms, particularly mood charts. Although all of these approaches are popular, only online psychotherapy and videogames have actually been evaluated in studies to evaluate both validity and efficacy. The face validity of social communication strategies including social media and blogs is strong, with clear implications for stigma reduction and peer support. Informational websites continue to be primary sources of psychoeducation on mental disorders. Social media sites have widespread use by the public and a profusion of health discussions and tools, but without published research evaluation of efficacy. SUMMARY: E-health strategies, particularly online psychotherapy and tools to document symptoms, are useful and likely effective. Social communication strategies show enormous popularity, but urgently require research evaluation for impact.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".