Suicide Prevention by Peers Offering Recovery Tactics (SUPPORT) for US Veterans With Serious Mental Illness: Community Engagement Approach
Bibliographic record
Abstract
BACKGROUND: Peer specialists are hired, trained, and accredited to share their lived experience of psychiatric illness to support other similar individuals through the recovery process. There are limited data on the role of peer specialists in suicide prevention, including their role in intervention development. OBJECTIVE: To better understand peer specialists within the Veterans Health Administration (VHA), we followed partnership community engagement and a formative research approach to intervention development to (1) identify barriers, facilitators, and perceptions of VHA peer specialists delivering a suicide prevention service and (2) develop and refine an intervention curriculum based on an evidence-informed preliminary intervention framework for veterans with serious mental illness (SMI). METHODS: Following the community engagement approach, VHA local and national peer support and mental health leaders, veterans with SMI, and veteran peer specialists met to develop a preliminary intervention framework. Next, VHA peer specialist advisors (n=5) and scientific advisors (n=6) participated in respective advisory boards and met every 2-4 months for more than 18 months via videoconferencing to address study objectives. The process used was a reflexive thematic analysis after each advisory board meeting. RESULTS: The themes discussed included (1) the desire for suicide prevention training for peer specialists, (2) determining the role of VHA peer specialists in suicide prevention, (3) integration of recovery themes in suicide prevention, and (4) difficulties using safety plans during a crisis. There were no discrepancies in thematic content between advisory boards. Advisor input led to the development of Suicide Prevention by Peers Offering Recovery Tactics (SUPPORT). SUPPORT includes training in general suicide prevention and a peer specialist-delivered intervention for veterans with SMI at an increased suicide risk. This training aims to increase the competence and confidence of peer specialists in suicide prevention and the intervention supports veterans with SMI at an increased suicide risk through their recovery process. CONCLUSIONS: This paper intends to document the procedures taken in suicide prevention intervention development, specifically those led by peer specialists, and to be a source for future research developing and evaluating similar interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT05537376; https://classic.clinicaltrials.gov/ct2/show/NCT05537376.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".