Exploring Counselor Practices and Risk Assessment in a Proactive Digital Intervention Through Instagram in Young People: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Suicide is one of the leading causes of preventable death in young people, and the way young people are communicating suicidality has evolved to include web-based disclosures and help-seeking. To date, mental health intervention services, both on the web and in person, have been conceived in the traditional model, whereby support is provided if a young person (or their family) actively seeks out that support when distressed. On the other hand, proactive outreach is an innovative approach to intervention that has been shown to be effective in other areas of health care. Live for Tomorrow chat was delivered on Instagram and comprised of counselors who reach out to provide brief person-centered intervention to young people who post content indicating distress or suicidality. OBJECTIVE: Our aim was to explore how counselors engaged young people in a proactive digital intervention and how risk assessment was conducted in this context. METHODS: We analyzed 35 transcripts of conversations between counselors and young people aged 13-25 years using the 6-step approach of Braun and Clarke's reflexive thematic analysis. These transcripts included a counseling intervention and a follow-up chat that was aimed at collecting feedback about the counseling intervention. RESULTS: A total of 7 themes emerged: using microskills to facilitate conversations, building confidence and capacity to cope with change, seeking permission when approaching conversations about suicidality or self-harm, conversations about suicidality following a structured approach, providing assurances of confidentiality, validation of the experience of suicidality, and using conversations about suicidality to identify interventions. Counselors were able to translate counseling microskills and structured questioning regarding suicidality into a digital context. In particular, in the digital context, counselors would use the young person's post and emojis to further conversations and build rapport. CONCLUSIONS: The findings highlight the importance of the counselor's role to listen, empathize, validate, and empower young people and that all these skills can be transferred to a digital text counseling intervention. Counselors used a structured approach to understanding suicidality in a permission-seeking, validating, and confidential manner to identify interventions with the young person. These practices allowed the conversation to move beyond traditional risk assessment practices to meaningful conversations about suicidality. Moving beyond traditional risk assessment practices and into conversations about suicidality allowed for the validation of the young person's experience and exploration of interventions and support that made sense and were seen to be helpful to the young person. This study highlighted the benefits of a proactive digital chat-based intervention, which is a novel approach to engaging with young people experiencing psychological distress and suicidality. Furthermore, this research demonstrates the feasibility and benefit of moving mental health intervention and support to a medium where young people are currently disclosing distress and intervening proactively.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".