Using Implementation Science to Design Strategies for Embedding a Suicide Safety Planning Digital Intervention into a Psychiatric Emergency Department
Bibliographic record
Abstract
Suicide is a significant health issue in Canada and worldwide. The emergency department (ED) is a key location for suicide prevention, and safety planning interventions (SPIs) are recognized as best practice for brief suicide prevention efforts. Traditionally, SPIs have been delivered in paper format, but there is a growing need to improve portability and explore alternative modalities to support accessible, ‘at hand’ safety plans. In response, the Centre for Addiction and Mental Health developed the Hope app in 2020 as a digital SPI tool. However, like many other digital mental health interventions around the world, the implementation of the Hope app has been limited. Existing literature continues to highlight non-systematic and non-rigorous implementation efforts, often not guided by theory, with the abundance of abandoned digital tools testifying to this reality. Research is needed to support tailored and systematic efforts for implementation, particularly in supporting clinicians’ behaviour change, as they are often the deliverers of digital interventions. Furthermore, integrated knowledge translation is an ideal way to conduct research to solve complex problems like implementation. In response, this doctoral dissertation work used an integrated knowledge translation approach and sought to implement the Hope app in a psychiatric ED, guided by several theoretical frameworks, including the Behaviour Change Wheel and the Knowledge to Action framework. Study 1 consolidated the current literature on digital interventions for suicide prevention implemented in clinical settings, highlighting critical gaps including limited qualitative evidence, insufficient reporting on implementation determinants, and a lack of long-term outcome evaluations, such as sustainability and penetration. Findings of Study 1 emphasized the need for rigorous strategies to integrate digital tools seamlessly into clinical workflows for lasting impact. Study 2 generated qualitative evidence on implementation determinants, offering a comprehensive understanding of behavioural influences such as motivation, capability, and the context (opportunity) in which clinicians practice that shape the adoption of digital tools in clinical settings. It also provided a knowledge base for Study 3, identifying areas of support needed to enhance successful implementation. In Study 3, 11 implementation strategies were identified, containing various intervention functions and behaviour change techniques to support clinicians' behaviour change. The evaluation of co-designers' experiences reflected overall positive collaboration. This research offers valuable insights that have led to the development of 11 implementation strategies for the Hope app to address existing barriers in the ED setting. It also outlines outcomes that must be monitored to ensure sustained use. As such, this research offers a model for developing tailored implementation strategies in collaboration with multiple partners, guided by an established theoretical framework, and helps bridge the gap between theory and practice while addressing the implementation challenges for digital 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 | 0.069 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".