Caught in the Web: A Review of Web-Based Suicide Prevention
Bibliographic record
Abstract
BACKGROUND: Suicide is a serious and increasing problem worldwide. The emergence of the digital world has had a tremendous impact on people's lives, both negative and positive, including an impact on suicidal behaviors. OBJECTIVE: Our aim was to perform a review of the published literature on Web-based suicide prevention strategies, focusing on their efficacy, benefits, and challenges. METHODS: The EBSCOhost (Medline, PsycINFO, CINAHL), OvidSP, the Cochrane Library, and ScienceDirect databases were searched for literature regarding Web-based suicide prevention strategies from 1997 to 2013 according to the modified PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. The selected articles were subjected to quality rating and data extraction. RESULTS: Good quality literature was surprisingly sparse, with only 15 fulfilling criteria for inclusion in the review, and most were rated as being medium to low quality. Internet-based cognitive behavior therapy (iCBT) reduced suicidal ideation in the general population in two randomized controlled trial (effect sizes, d=0.04-0.45) and in a clinical audit of depressed primary care patients. Descriptive studies reported improved accessibility and reduced barriers to treatment with Internet among students. Besides automated iCBT, preventive strategies were mainly interactive (email communication, online individual or supervised group support) or information-based (website postings). The benefits and potential challenges of accessibility, anonymity, and text-based communication as key components for Web-based suicide prevention strategies were emphasized. CONCLUSIONS: There is preliminary evidence that suggests the probable benefit of Web-based strategies in suicide prevention. Future larger systematic research is needed to confirm the effectiveness and risk benefit ratio of such strategies.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".