Applying Trauma-Informed Research Guidelines to Qualitative Health Research: Techniques for Building Researcher Confidence and Skills
Bibliographic record
Abstract
This article builds on our experience conducting qualitative health research to provide specific trauma-informed interview skills for researchers. Our discussion, contextualized through the Trauma-Informed Qualitative Research Guidelines (TIRGs) developed in an earlier article, uses composite examples to illustrate specific interview approaches. We focus on three TIRGs: preparing for the qualitative interview, extending safety and trust during the qualitative interview, and recognizing when to change course to avoid re-traumatization. By emphasizing skills directly related to these TIRGs, we aim to equip qualitative health researchers with practical techniques that boost their confidence in conducting trauma-informed qualitative interviews. When implemented thoughtfully and intentionally, the TIRGs can improve participants’ safety, ensure their autonomy, and help them recount their experiences without reliving them.
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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 | 0.695 | 0.409 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.007 |
| 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; both teacher heads agree on what is shown here.
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".