Advancing Health Equity and Social Justice in Forensic Nursing Research, Education, Practice, and Policy: Introducing Structural Violence and Trauma- and Violence-Informed Care
Bibliographic record
Abstract
Initial conceptualizations of violence and trauma in forensic nursing have remained relatively narrowly defined since the specialty's inception. The advent of trauma-informed care has been important but has limitations that obfuscate social and structural determinants of health, equity, and social justice. As forensic nursing practice becomes more complex, narrow definitions of violence and trauma limit the effectiveness of trauma-informed care in its current incarnation. In keeping with the nursing model of holistic care, we need ways to teach, practice, and conduct research that can accommodate these increasing levels of complexity, including expanding our conceptualizations of violence and trauma to advance health equity and social justice. The objective of this article is to introduce the concepts of structural violence and trauma- and violence-informed care as equity-oriented critical paradigms to embrace the increasing complexity and health inequities facing forensic nursing practice.
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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 | 0.099 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.011 | 0.061 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".