A Patient-Centered Forensic Nursing Model of Care for Victims of Law Enforcement Violence
Bibliographic record
Abstract
Background: The manuscript examines the nature, manifestations, and potential causes of law enforcement violence as well the need for a model of care for victims. Specifically, it explores development of a preliminary forensic nursing model of care. The questions posed over the course of development of the model follow (1) What are the challenges to developing a rudimentary forensic nursing model of care for victims of law enforcement violence? (2) What are the tenets to be utilized in developing the model? (3) What additional recommendations are to be considered in refining and expanding the model? Key Concept: A review of the literature in forensic nursing found a gap in care for victims of law enforcement violence. To address the gap given the lack of research, a preliminary model of care was developed based on key constructs from the following established models: (1) Theory of Abolition, (2) Critical Race Theory, (3) Levels of Racism, (4) Intersectionality, (5) Social Determinants of Health, (6) Emancipatory Praxis - Theory of Forensic Nursing, (7) Trauma-Informed Model of Care, and (8) Patient-Centered Model of Care. Implications for practice: The preliminary model developed adheres to the International Council of Nurses guidelines, which emphasize the nurse's duty to care without judgment or bias. Protocols established must be followed precisely to mitigate potential conflicts of interest in care of the victim. A practical application algorithm was developed based on care provided to other victims of violence. Conclusion: The model developed was focused on forensic nursing care. There is a need for further refinement involving an interdisciplinary approach. There is also a need for additional research as it relates to forensic nursing's role in caring for victims of law enforcement violence.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".