Utilisation and application of implementation science in complex suicide prevention interventions: A systematic review
Bibliographic record
Abstract
OBJECTIVES: Little is known about how complex, multilevel, and multicomponent suicide prevention interventions work in real life settings. Understanding the methods used to systematically adopt, deliver, and sustain these interventions could ensure that they have the best chance of unfolding their full effect. This systematic review aimed to examine the application and extent of utilisation of implementation science in understanding and evaluating complex suicide prevention interventions. METHODS: The review adhered to updated PRISMA guidelines and was prospectively registered with PROSPERO (CRD42021247950). PubMed, CINAHL, PsycINFO, ProQuest, SCOPUS and CENTRAL were searched. All English-language records (1990-2022) with suicide and/or self-harm as the primary aims or targets of intervention were eligible. A forward citation search and a reference search further bolstered the search strategy. Interventions were considered complex if they consisted of three or more components and were implemented across two or more levels of socio-ecology or levels of prevention. RESULTS: One hundred thirty-nine records describing 19 complex interventions were identified. In 13 interventions, use of implementation science approaches, primarily process evaluations, was explicitly stated. However, extent of utilisation of implementation science approaches was found to be inconsistent and incomprehensive. LIMITATIONS: The inclusion criteria, along with a narrow definition of complex interventions may have limited our findings. CONCLUSION: Understanding the implementation of complex interventions is crucial for unlocking key questions about theory-practice knowledge translation. Inconsistent reporting and inadequate understanding of implementation processes can lead to loss of critical, experiential knowledge related to what works to prevent suicide in real world settings.
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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 | 0.159 | 0.414 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".