Designing Implementation Strategies for a Digital Suicide Safety Planning Intervention in a Psychiatric Emergency Department: Protocol for a Multimethod Research Project
Bibliographic record
Abstract
BACKGROUND: Suicide prevention is currently a national health priority in Canada. Emergency departments (EDs) are critical settings for suicide prevention, and in our local psychiatric ED at the Centre for Addiction and Mental Health, we plan to embed an app-based tool called the Hope app to support suicide safety planning intervention. The app is free and available on app stores, and usability tests have been completed. As a next step to embed this new tool into the routine clinical workflow, research is needed to assess determinants of and design strategies for implementation with the end goal of routinization. OBJECTIVE: The purpose of this 2-phased research is to implement the app in the routine clinical workflow in our local psychiatric ED. The specific objectives are as follows: (1) understanding ED clinicians' perceptions and experience of implementing the app in routine practice and identifying barriers to and facilitators of implementation (phase 1) and (2) using findings and outputs from phase 1 and collaborating with service users, families, and ED clinicians to co-design implementation strategies for the app (phase 2). METHODS: We will use an integrated knowledge translation approach throughout this project. In phase 1, we will conduct interviews with ED clinicians to identify implementation determinants using a behavior change framework. In phase 2, a co-design team comprising clinicians, ED service users, and families will design implementation strategies that align with the determinants identified in phase 1. RESULTS: This protocol presents detailed information about the entire structure of the 2-phased research project. Ethics approval for conducting the qualitative descriptive study (phase 1) has been obtained, and the recruitment and data collection processes will be completed no later than December 2023. Ethics approval for phase 2 is underway. CONCLUSIONS: Involving multiple knowledge user groups early in the research and decision-making process is crucial for successful implementation. Although co-designing is commonly practiced during innovation development, there is often a misconception that the responsibility for implementing what has been designed falls on others. This research aims to fill this methodological gap in the health informatics literature. By the end of this project, we will have developed theory-informed implementation strategies to support Centre for Addiction and Mental Health ED clinicians in adopting the Hope app to complete safety planning intervention. These strategies, guided by a behavior change framework, will target clinicians' behavior change and seamlessly integrate the app into the routine clinical workflow. In addition, this research project will provide recommendations on how to involve multiple knowledge user groups and offer insights into how the methodology used can be adapted to other areas within the health informatics literature. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/50643.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.083 | 0.077 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.079 | 0.013 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".