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Record W4409818564 · doi:10.1136/bmjph-2024-001206

Towards development of guidelines for harnessing implementation science for suicide prevention: an international Delphi expert consensus study

2025· article· en· W4409818564 on OpenAlexaff
Sadhvi Krishnamoorthy, Gregory Armstrong, Victoria Ross, Lennart Reifels, Hayley Purdon, Jill Francis, Jacinta Hawgood, Sharna Mathieu, Alexandr Kasal, Allison Crawford, Allison M. Gustavson, András Székely, Anna Baran, Ashley Nemiro, Chez Curnow, Daniel J. Reidenberg, Daria Biechowska, Ella Arensman, Emmanuel Nii‐Boye Quarshie, Fiona Shand, Caroline M. Ramirez, Isabel Zbukvic, Jorgen Gullestrup, Katherine McGill, Kylie King, Lakshmi Vijayakumar, Lauren White, Loraine Barnaby, Mark Sinyor, Marlena Sokół-Szawłowska, Maryke Van Zyl, Merike Sisask, Michael M. Phillips, Mohsen Rezaeian, Naohiro Yonemoto, Nathaniel J. Pollock, Nikhil Jain, Paul Yip, Ping Qin, Piotr Toczyski, Rakhi Dandona, Ricardo Gusmão, Samah Jabr, Sarah G. Spafford, Tae-Yeon Hwang, Thomas Niederkrotenthaler, Ulrich Hegerl, Vita Poštuvan, Yutaka Motohashi, Kairi Kõlves

Bibliographic record

VenueBMJ Public Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of TorontoSunnybrook Health Science CentreMemorial University of NewfoundlandCentre for Addiction and Mental HealthHealth Sciences CentreOttawa Hospital
FundersGriffith UniversityDepartment of Health and Aged Care, Australian GovernmentAustralian Government
KeywordsThematic analysisContext (archaeology)Delphi methodPsychological interventionMedical educationKnowledge translationStakeholderPsychologyIntervention (counseling)MedicineQualitative researchPublic relationsKnowledge managementNursingPolitical scienceComputer scienceSociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.895
GPT teacher head0.799
Teacher spread0.096 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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