Toward a trauma-informed qualitative research approach: Guidelines for ensuring the safety and promoting the resilience of research participants
Bibliographic record
Abstract
Qualitative researchers frequently conduct studies with individuals who have experienced various types of trauma, including those who have been historically marginalized and oppressed. However, in-depth discussions of how to conduct trauma-informed qualitative research do not exist. Thus, we lay the groundwork for a trauma-informed qualitative approach and then outline five guidelines for conducting research: (1) preparing for community entry: Learning about the impacts of traumatic events and historical trauma on individuals and communities; (2) preparing for the qualitative interview or focus group: Establishing safety and trust in the research environment; (3) extending safety and trust into the qualitative interview or focus group; (4) knowing when to change course to avoid re-traumatization in the interview or focus group; and (5) committing to regular and radical self-reflection and self-care in the research process. To demonstrate their applicability, we use an example from our own research with multiply-marginalized queer and transgender migrants in South Africa. This article advances the study of qualitative methods, offering researchers an opportunity to incorporate these guidelines into their study design and implementation to ensure participant safety and promote their resilience.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.445 | 0.390 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".