Developing theory-informed implementation strategies to embed a suicide safety planning intervention app into a psychiatric emergency department: co-design study using the Behaviour Change Wheel
Bibliographic record
Abstract
BACKGROUND: Safety planning is a commonly used, evidence-based intervention for suicide prevention. There is a need for continuous engagement with safety plans post-discharge, and the improvement of safety plan portability has been discussed within our mental health organisation. This has led to the development of an app, called the Hope App. This study aims to implement this app into routine practice in a Canadian psychiatric emergency department. AIMS: We aimed to describe a collaborative, theoretically driven approach to co-design implementation strategies to elicit behaviour change among emergency department clinicians; co-develop a set of tailored, theory-informed, multifaceted implementation strategies for embedding an app into a psychiatric emergency department; and describe engagement evaluation received by the co-design team. METHOD: Co-design approaches and the Behaviour Change Wheel were used to develop implementation strategies with clinicians, patients and care partners. The co-design team consisted of 12 members, and we held four design sessions. Design sessions were iterative in nature and organised such that the findings of each session fed into the next session. RESULTS: We identified 11 implementation strategies encompassing different combinations of intervention functions and behaviour change techniques, targeting barriers and leveraging facilitators identified in our previous work. CONCLUSIONS: The tailored implementation strategies developed in this study have the potential to fill existing gaps in integrating digital technology. A key strength of this study is its use of behaviour change theories and a collaborative approach. The strategies are designed to align with the needs and preferences of clinicians, patients and care partners.
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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 | 0.083 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".