FORENSIC-CORRECTIONAL PSYCHIATRIC SERVICES IN ABU DHABI: LESSONS FROM A DESCRIPTIVE ANALYIS OF THE ATTRIBUTES OF A SAMPLE OF SERVICE USERS
Bibliographic record
Abstract
BACKGROUND: Forensic-correctional psychiatric services are an important component of the public mental health services that provide care to offenders with mental illness in the criminal justice system and conduct psycho-legal assessments. Although forensic-correctional psychiatric services have evolved in Abu Dhabi, more work is needed in providing adequate mental health care for offenders. METHODS: This study provides a situational analysis of forensic-correctional psychiatric services in Abu Dhabi. We included a descriptive analysis of the data collected on service users admitted for psycho-legal assessments and treatment in the forensic-correctional units and those reviewed in the medical board units for issuing court reports. The study spanned the period between January 2019 to October 2020. RESULTS: A total of 398 males were included in the study. The participants' mean age was 35.3 (SD 9.27) years and were predominantly single, unemployed and high school graduates. The most prevalent diagnosis was schizophrenia spectrum disorder, (n=129, 31.6%). The mean length of stay in the forensic-correctional unit was 11.07 days. As many as 82.4% of the participants were referred for evaluation. The most common type of crime was categorized as "abnormal behaviour" under the code of practice number 511 of the list of crimes as per the general prosecutor of the United Arab Emirates followed by violence. CONCLUSION: Considering the level of demand for services and the limited number of forensic-correctional health professionals, there is a need for more resources to develop expertise, clinical services and infrastructures to expand the practice of forensic-correctional psychiatry. The creation of a universal database for all forensic-correctional psychiatric services is needed to better understand the unmet mental health needs. An additional investment of resources for research to inform mental health policy, laws and practice is indicated. Optimally, the lessons highlighted in this study can guide action plans for improving forensic-correctional mental health services in comparable settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".