Using Implementation Science to Design Strategies for Embedding a Suicide Safety Planning Digital Intervention into a Psychiatric Emergency Department
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,069 | 0,096 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,007 |
| Communication savante | 0,009 | 0,007 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».