The Emergence of Forensic Nursing in India: Implications for Healthcare and Criminal Justice
Bibliographic record
Abstract
Sir, The emergence of forensic nursing as a specialised field in India represents a significant advancement in healthcare and criminal justice. This development addresses the rising global incidence of violence and abuse, which significantly affects victims’ physical, mental and emotional well-being.[1] The National Health Policy (2017) emphasises the need for specialised healthcare professionals to care for victims of violence, abuse and neglect across all age groups.[2] NURSES’ ROLE IN MANAGING VICTIMS OF VIOLENCE AND CRIME Nurses play a crucial role in managing victims of violence and crimes. Forensic nurses combine forensic science with clinical nursing practices for law enforcement. The Indian Nursing Council’s initiative to introduce a post-graduate programme (MSc Nursing) in forensic nursing as a speciality course in October 2024 will equip nurses with a broad understanding of the medicolegal aspects of forensic science, judicial systems and legal procedures; skills to conduct/assist forensic medical experts in assessing cases of sexual assault, trauma and causes of death; the ability to provide emergency interventions to victims of violence/crime and trauma and expertise in developing, implementing and promoting standardised protocols for responding to victims and perpetrators of accidents, trauma and crime.[3] INTERNATIONAL PERSPECTIVE Forensic nursing has been successfully implemented in several countries, providing valuable insights into India’s implementation strategy. The United States pioneered this field in the 1970s and now offers various specialisations, including Sexual Assault Nurse Examiners. Canada has integrated forensic nursing into emergency departments and community health centres. The United Kingdom has developed specialised forensic mental health nursing roles, whereas Australia has established forensic nurse consultant positions in major hospitals. FUTURE IMPLICATIONS The implementation of forensic nursing in the Indian healthcare system presents both challenges and opportunities for the growth and improvement of healthcare and criminal justice systems. Challenges may include integrating existing healthcare structures, ensuring adequate resources and funding, addressing potential cultural barriers to discussing sensitive topics and establishing clear protocols for collaboration between healthcare and legal systems. Opportunities include the development of specialised training programmes, the creation of new job roles and the establishment of dedicated forensic nursing units in hospitals. In addition, it may foster interdisciplinary collaboration between healthcare professionals, law enforcement and legal experts, potentially resulting in more comprehensive and effective approaches to address violence and abuse cases. This field also offers opportunities for research and innovation in forensic techniques tailored to the Indian context, which could contribute to the global body of knowledge in forensic nursing. CONCLUSION The introduction of forensic nursing in India has the potential to revolutionise the care provided to victims of violence and abuse, enhance the quality of forensic evidence collection and strengthen the link between healthcare and legal systems. As India embarks on this journey, the integration of forensic nursing into the healthcare system promises to not only improve patient outcomes but also contribute to a more just and equitable society. The success of this initiative will depend on continued support, resources and collaboration across multiple sectors, ultimately leading to a safer and healthier India. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 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".