Maintaining Safety While Discussing Suicide: Trauma Informed Research in an Online Focus Groups
Bibliographic record
Abstract
Although trauma informed (TI) care has been well researched and is used in many disciplines, TI practices for research are less developed. In this article, we explore the use of TI practices when discussing the sensitive topic of suicide within an online focus group. Qualitative studies on rural suicide are sparse, even though the incidence of suicide is higher rurally than in urban areas. Rural communities are often close knit and stigma can be greater toward non-normative experiences such as mental illness and suicide. Due to the nature of rural communities, the trauma of suicide can affect many people. We conducted focus groups with rural community participants who had an interest in suicide prevention to explore the gaps in rural suicide research and the best methods for knowledge dissemination of existing research. Steps were taken to mitigate re-traumatization and/or severe distress in the participants through a TI research approach. An online video conferencing platform became necessary due to the COVID-19 pandemic. The online features promoted safety and transparency by: enabling participants to turn off camera and microphone if they became distressed, allowing them time to self-regulate until feeling sufficiently safe to return to the focus group discussion; leaving the discussion at any time with little disruption; and being able to choose a comfortable place to join the discussion. Other TI activities included ensuring ongoing consent throughout the process, recruiting through a third party to enhance safety, having support resource lists tailored to the region, and encouraging participants to share and debrief final thoughts. A number of participants commented on feeling safe within the environment of the focus group. Limitations included challenges identifying distress online and technological difficulties associated with rural internet services. To our knowledge, this is the first article using a TI approach for discussing suicide through an online method.
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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 | 0.057 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".