Information and communication technology-based interventions for suicide prevention implemented in clinical settings: A scoping review protocol
Bibliographic record
Abstract
IntroductionThere is a surplus of Information and Communication Technology (ICT)-based interventions for suicide prevention. However, it is unclear which of these ICT-based interventions for suicide prevention have been implemented in clinical settings. Furthermore, evidence shows that implementation strategies have often been mismatched to existing barriers. In response, the authors recognize the critical need for prospectively assessing the barriers and facilitators and then strategically developing implementation strategies. This review is part of a multi-phase project to develop and test tailored implementation strategies for mobile app-based suicide prevention in clinical settings. The overall objective of this scoping review is to identify and characterize ICT-based interventions for all levels of suicide prevention in clinical settings. Additionally, this review will identify and characterize the barriers and facilitators to implementing these ICT-based interventions as well as reported measures and outcomes. The findings will directly inform the subsequent phase to maximize implementation and inform future efforts for implementing other types of ICT-based interventions related to suicide prevention in clinical settings.Methods and analysis This review will adhere to the methods described by the Joanna Briggs Institute for conducting scoping reviews. The reporting will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews checklist. The following databases will be searched: Medline, PsycInfo, Embase, CINAHL, Web of Science, and LISTA. Two reviewers will independently screen the articles and extract data using a standardised data collection tool. Then, authors will characterize extracted data using frameworks, typology, and taxonomies to address the proposed review questions. Ethics and disseminationEthics approval is not required for this scoping review. Authors will share the results in a peer-reviewed, open access publication, and conference presentations. Furthermore, the findings will be shared with relevant health organizations through lay language summaries and informal presentations.
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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 | 0.101 | 0.078 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.097 | 0.019 |
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".