Method Matters: Integrating Trauma-Informed Principles into Psychiatric and Mental Health Nursing Research
Bibliographic record
Abstract
Psychiatric and mental health (PMH) nurses integrate the concept of trauma-informed care into practice, policy, and education. Despite the frequency of PMH nurses practicing in a trauma-informed manner, there is a paucity of literature focused on integrating trauma-informed principles into research methods. Professions outside of the nursing sphere, specifically social work and social sciences, predominate the discourse around trauma-informed research. The authors of this manuscript provide detailed methods on a project using trauma-informed qualitative research methods with a feminist perspective. Semi-structured interviews with ten individuals with an experience of sexual violence answered the research question: what is the retrospective experience of women who encountered sexual violence in post-secondary education? An important part of the research design was an informal debrief with the audio recorder off, after the interview. Field notes were taken within this debrief, and participants reviewed these field notes as part of the member-checking process. By explaining the methods used in detail, referencing the available literature, and using the critical reflection of participants captured in the field notes, the authors of this manuscript explore strengths, conflicts, and boundary issues PMH nurses need to consider when integrating trauma-informed research methods into their research practices.
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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 | 0.494 | 0.422 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier 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".