The Effectiveness of Internet-Based Self-Help Interventions to Reduce Suicidal Ideation: Protocol for a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Suicidal ideation is a highly prevalent condition. There are several barriers for individuals to seek treatment that may be addressed by providing internet-based self-help interventions (ISIs). Current evidence suggests that ISIs for mental disorders may only be effective in reducing suicidal ideation if they specifically target suicidal thoughts or behaviors. OBJECTIVE: The aim of this systematic review and meta-analysis is to investigate the effectiveness of ISIs that directly target suicidal thoughts or behaviors. METHODS: We will conduct a sensitive systematic literature search in PsycINFO, MEDLINE, the Cochrane Central Register of Controlled Trials, and the Centre for Research Excellence of Suicide Prevention databases. Only randomized controlled trials evaluating the effectiveness of ISIs for suicide prevention will be included. Interventions must be delivered primarily in a Web-based setting; mobile-based interventions and interventions targeting gatekeepers will be excluded. Suicide ideation will be the primary outcome; secondary outcomes will be completed suicides, suicide attempts, depressiveness, anxiety, and hopelessness. Study quality will be assessed using the Cochrane Risk of Bias tool. We will provide a narrative synthesis of included studies. If studies are sufficiently homogenous, we will conduct a meta-analysis of the effectiveness on suicide ideation and, if possible, we will evaluate publication bias using funnel plots. We will evaluate the cumulative evidence in accordance with the Grading of Recommendations Assessment, Development and Evaluation framework. RESULTS: This review is in progress, with findings expected by August 2019. CONCLUSIONS: This systematic review and meta-analysis focuses on the effectiveness of ISIs for suicidal thoughts and behaviors. It will provide guidance to clinical practice and encourage further research by synthesizing the best available evidence. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) CRD42019130253; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=130253. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/14174.
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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 | 0.064 | 0.085 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.025 | 0.040 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.067 | 0.007 |
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".