Towards development of guidelines for harnessing implementation science for suicide prevention: an international Delphi expert consensus study
Bibliographic record
Abstract
Objectives: Suicide research and prevention are complex. Many practical, methodological and ethical challenges must be overcome to implement effective suicide prevention interventions. Implementation science can offer insights into what works, why and in what context. Yet, there are limited real-world examples of the application of implementation science in suicide prevention. This study aimed to identify approaches to employ principles of implementation science to tackle important challenges in suicide prevention. Methods: A questionnaire about promoting implementation science for suicide prevention was developed through thematic analysis of stakeholder narratives. Statements were categorised into six domains: research priorities, practical considerations, approach to intervention design and delivery, lived experience engagement, dissemination and the way forward. The questionnaire (n=52 statements-round 1; n=44 statements-round 2; n=9 statements-round 3) was administered electronically to a panel (n=62-round 1, n=48-round 2; n=45-round 3) of international experts (suicide researchers, leaders, project team members, lived experience advocates). Statements were rated on a Likert scale based on an understanding of importance and priority of each item. Statements endorsed by at least 85% of the panel would be included in the final guidelines. Results: Eighty-two of the 90 statements were endorsed. Recommendations included broadening research inquiries to understand overall programme impact; accounting for resources in the translation of evidence into practice; embedding implementation science in intervention delivery and design; meaningfully engaging lived experience; considering channels for dissemination of implementation-related findings and focusing on next steps needed to routinely harness the strengths of implementation science in suicide prevention research, practice and training. Conclusion: An interdisciplinary panel of suicide prevention experts reached a consensus on optimal strategies for using implementation science to enhance the effectiveness of policies and programmes aimed at reducing suicide.
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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 | 0.419 | 0.329 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.008 | 0.028 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".