Acceptability, Safety, and Resonance of the Pilot Digital Suicide Prevention Campaign “Better Off With You”: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The Interpersonal Theory of Suicide posits that there are three key elements of suicidal behavior: perceived burdensomeness, thwarted belongingness, and the acquired capability for suicide. The digital campaign Better Off With You was developed to directly challenge the idea of perceived burdensomeness among people who are contemplating suicide in 2 communities within Australia. OBJECTIVE: The aim of this study is to explore the needs and preferences of people with lived experience of suicidal thoughts and actions to inform the development of Better Off With You. METHODS: This study involved a series of focus groups that aimed to discuss campaign messaging, scope, and approach. People with lived experience of suicidal thoughts and actions attended the focus groups. After the completion of initial focus groups, the results informed the creation of campaign collateral by creative agencies. Early versions of the campaign collateral were then presented in the user testing sessions. Transcriptions were analyzed via thematic analysis. RESULTS: In total, 13 participants attended the focus groups and 14 attended the user testing sessions. The following three overarching themes were presented: acceptability, safety, and resonance. Participants believed that suicide is a serious and ongoing issue in their communities and welcomed a localized suicide prevention focus via peer-to-peer storytelling. The idea of perceived burdensomeness required clarification but was perceived as acceptable and relevant. Participants seemed drawn toward peer narratives that they perceived to be authentic, genuine, and believable as given by real people with lived experience. Campaign messaging needs to be clear and empathetic while directly talking about suicide. Participants did not anticipate any significant negative or harmful impact from any campaign videos and highlighted the importance of providing appropriate help-seeking information. CONCLUSIONS: This iterative study provided important insights and knowledge about peer-to-peer storytelling in suicide prevention campaigns. Future campaigns should involve simple messaging, be validating and empathetic, and consider including a lived experience perspective.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".