Weaving Structural Violence into Trauma-Informed Qualitative Health Research with Populations Considered Vulnerable
Bibliographic record
Abstract
Trauma-informed approaches (TIA) are widely used in sensitive research to prevent re-traumatization and support the well-being of participants, often labelled as hard-to-reach or hidden populations . These labels reflect an individual-level framing, implying that certain groups have made themselves difficult to engage rather than acknowledging the structural barriers that limit their access to services and research participation. Trauma is deeply interconnected with social exclusion and systemic inequities. To address these limitations, researchers can further enhance trauma-informed research through a trauma- and violence-informed care (TVIC) approach, which emphasizes the structural and interpersonal violence experienced by populations considered vulnerable. A TVIC lens challenges deficit-based narratives by shifting focus away from individual responsibility and toward the structural violence that creates these conditions. This paper reflects on a qualitative study exploring burn injuries among underserved communities, particularly Indigenous Peoples and individuals experiencing homelessness or unstable housing. Burn injuries and fire-related incidents are often attributed to individual behaviour, obscuring the structural conditions that contribute to these outcomes. The paper uses researcher experiences to inform understanding of the need for trauma- and violence-informed qualitative research. Data collection involved document analysis, qualitative interviews, participant observations, and informal conversations, with the researcher using lessons learned to highlight the importance of applying a TVIC approach with populations considered vulnerable across all research stages—from researcher preparation and interview methodologies to sustained participant engagement. The lessons learned during the research underscore the need to move beyond individual-level framings of trauma to recognize structural determinants shaping lived and living experiences. A TVIC approach enhances equity-oriented research methodologies, equipping researchers to critically engage with systemic forces influencing participants’ realities. A structural perspective can help researchers develop methods, practices, and analyses that drive transformative and structurally oriented solutions.
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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 | 0.180 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.015 | 0.040 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".