Implementation of Complex Suicide Prevention Interventions: Insights into Barriers, Facilitators and Lessons Learned
Bibliographic record
Abstract
INTRODUCTION: Effective suicide prevention interventions are infrequently translated into practice and policy. One way to bridge this gap is to understand the influence of theoretical determinants on intervention delivery, adoption, and sustainment and lessons learned. This study aimed to examine barriers, facilitators and lessons learned from implementing complex suicide prevention interventions across the world. METHODS AND MATERIALS: This study was a secondary analysis of a systematic review of complex suicide prevention interventions, following updated PRISMA guidelines. English published records and grey literature between 1990 and 2022 were searched on PubMed, CINAHL, PsycINFO, ProQuest, SCOPUS and CENTRAL. Related reports were organized into clusters. Data was extracted from clusters of reports on interventions and were mapped using the updated Consolidated Framework for Implementation Research. RESULTS: The most frequently-reported barriers were reported within the intervention setting and were related to the perceived appropriateness of interventions within settings; shared norms, beliefs; and maintaining formal and informal networks and connections. The most frequently reported facilitators concerned individuals' motivation, capability/capacity, and felt need. Lessons learned focused on the importance of tailoring the intervention, responding to contextual needs and the importance of community engagement throughout the process. CONCLUSION: This study emphasizes the importance of documenting and analyzing important influences on implementation. The complex interplay between the contextual determinants and implementation is discussed. These findings contribute to a better understanding of barriers and facilitators salient for implementation of complex suicide prevention interventions.
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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 | 0.033 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".