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Record W3005472839 · doi:10.2196/17520

Developing a Suicide Prevention Social Media Campaign With Young People (The #Chatsafe Project): Co-Design Approach

2020· article· en· W3005472839 on OpenAlexvenueno aff
Pinar Thorn, Nicole T. M. Hill, Michelle Lamblin, Zoe Teh, Rikki Battersby-Coulter, Simon Rice, Sarah Bendall, Kerry Gibson, Summer May Finlay, Ryan Blandon, Libby de Souza, Ashlee West, Anita Cooksey, Joe Sciglitano, Simon Goodrich, Jo Robinson

Bibliographic record

VenueJMIR Mental Health · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilUniversity of MelbourneAustralian Rotary HealthMedical Research CouncilAustralian Government
KeywordsThematic analysisSocial mediaPsychological interventionPopulationHarmSuicide preventionPsychologyTarget audienceMedical educationPoison controlQualitative researchPublic relationsMedicineSociologySocial psychologyMedical emergencyAdvertisingPolitical scienceEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Young people commonly use social media platforms to communicate about suicide. Although research indicates that this communication may be helpful, the potential for harm still exists. To facilitate safe communication about suicide on social media, we developed the #chatsafe guidelines, which we sought to implement via a national social media campaign in Australia. Population-wide suicide prevention campaigns have been shown to improve knowledge, awareness, and attitudes toward suicide. However, suicide prevention campaigns will be ineffective if they do not reach and resonate with their target audience. Co-designing suicide prevention campaigns with young people can increase the engagement and usefulness of these youth interventions. OBJECTIVE: This study aimed to document key elements of the co-design process; to evaluate young people's experiences of the co-design process; and to capture young people's recommendations for the #chatsafe suicide prevention social media campaign. METHODS: In total, 11 co-design workshops were conducted, with a total of 134 young people aged between 17 and 25 years. The workshops employed commonly used co-design strategies; however, modifications were made to create a safe and comfortable environment, given the population and complexity and sensitivity of the subject matter. Young people's experiences of the workshops were evaluated through a short survey at the end of each workshop. Recommendations for the campaign strategy were captured through a thematic analysis of the postworkshop discussions with facilitators. RESULTS: The majority of young people reported that the workshops were both safe (116/131, 88.5%) and enjoyable (126/131, 96.2%). They reported feeling better equipped to communicate safely about suicide on the web and feeling better able to identify and support others who may be at risk of suicide. Key recommendations for the campaign strategy were that young people wanted to see bite-sized sections of the guidelines come to life via shareable content such as short videos, animations, photographs, and images. They wanted to feel visible in campaign materials and wanted all materials to be fully inclusive and linked to resources and support services. CONCLUSIONS: This is the first study internationally to co-design a suicide prevention social media campaign in partnership with young people. The study demonstrates that it is feasible to safely engage young people in co-designing a suicide prevention intervention and that this process produces recommendations, which can usefully inform suicide prevention campaigns aimed at youth. The fact that young people felt better able to safely communicate about suicide on the web as a result of participation in the study augurs well for youth engagement with the national campaign, which was rolled out across Australia. If effective, the campaign has the potential to better prepare many young people to communicate safely about suicide on the web.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.215
GPT teacher head0.448
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations109
Published2020
Admission routes1
Has abstractyes

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