Mobile Health Technology Interventions for Suicide Prevention: Protocol for a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Previous research has reported that two of the major barriers to help-seeking for individuals at risk of suicide are stigma and geographical isolation. Mobile technology offers a potential means of delivering evidence-based interventions with greater specificity to the individual, and at the time that it is needed. Despite documented motivation by at-risk individuals to use mobile technology to track mental health and to support psychological interventions, there is a shortfall of outcomes data on the efficacy of mobile health (mHealth) technology on suicide-specific outcomes. OBJECTIVE: The objective of this study is to develop a protocol for a systematic review and meta-analysis that aims to evaluate the effectiveness of mobile technology-based interventions for suicide prevention. METHODS: The search includes the Cochrane Central Register of Controlled Trials (CENTRAL: The Cochrane Library), MEDLINE, Embase, PsycINFO, CRESP and relevant sources of gray literature. Studies that have evaluated psychological or nonpsychological interventions delivered via mobile computing and communication technology, and have suicidality as an outcome measure will be included. Two authors will independently extract data and assess the study suitability in accordance with the Cochrane Collaboration Risk of Bias Tool. Studies will be included if they measure at least one suicide outcome variable (ie, suicidal ideation, suicidal intent, nonsuicidal self-injurious behavior, suicidal behavior). Secondary outcomes will be measures of symptoms of depression. Where studies are sufficiently homogenous and reported outcomes are amenable for pooled synthesis, meta-analysis will be performed. A narrative synthesis will be conducted if the data is unsuitable for a meta-analysis. RESULTS: The review is in progress, with findings expected by summer 2018. CONCLUSIONS: To date, evaluations of mobile technology-based interventions in suicide prevention have focused on evaluating content as opposed to efficacy. Indeed, previous research has identified mobile applications that appear to present harmful content. The current review will address a gap in the literature by evaluating the efficacy of stand-alone mobile technology tools in suicide prevention. It is imperative that research identifies the evidence base for such tools in suicide prevention in order to inform policy, guide clinical practice, inform users and focus future research. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42017072899; https:// www.crd.york.ac.uk/prospero/display_record.asp?ID=CRD42017072899 (Archived by WebCite at http://www.webcitation.org/ 6tZAj0yqJ).
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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 | 0.068 | 0.099 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.031 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.092 | 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".