Toward the Establishment of a Forensic Nursing Specialty in Brazil
Bibliographic record
Abstract
BACKGROUND: Over the past two decades, Brazil has made progress in bringing political and community attention to issues related to violence. The recognition of links between violence and health has intensified calls to accelerate the development of a forensic nursing specialty in Brazil. AIM: The aim of this study was to systematically examine and synthesize the literature on the development of the forensic nursing specialty around the globe and to extract important lessons for the establishment of a forensic nursing specialty in Brazil. METHOD: An integrative review was conducted according to the method described by Whittmore and Knafl (2005). Electronic searches of the following databases were conducted between December 2012 and March 2013: CINAHL Plus with Full Text, Criminal Justice, Index to Legal periodicals, MEDLINE, Soc Index with Full Text, Social Work Abstracts, SCOPUS, and PsycINFO. The search terms used were: [(TI nurs* or SU nurs*) and [TI (forensic* or penal or prison*) or SU (forensic* or penal or prison*)] and (sexual assault nurse examiner*). Preestablished inclusion/exclusion criteria were used to select published articles for review. RESULTS: Twenty-three articles met inclusion criteria and were included in the full review. Important lessons for Brazil are discussed in terms of education and curricular issues and forensic psychiatric nursing. CONCLUSIONS: In Brazil, there is a window of opportunity to contribute the theoretical foundations of forensic nursing science and to advance nursing specialty practice in the areas of Sexual Assault Nurse Examiners and forensic psychiatric nurses.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".