Forensic psychiatry in Pakistan: Where next following the Supreme Court judgement
Bibliographic record
Abstract
Introduction No statutory mental health services exist for justice-involved individuals in Pakistan. The lack of expertise in forensic psychiatry serves to deny individuals with mental illness the critical support needed for mental healthcare and adequate court dispositions with serious unintended consequences including capital punishment for those who could otherwise be deemed treatment and not punishment worthy. A landmark judgement by the Supreme Court of Pakistan in February 2021 criticized the lack of forensic psychiatry expertise in Pakistan and directing the development of forensic mental health services and forensic psychiatry training in Pakistan. Objectives The key objectives are: 1. Understanding the timeline of how justice invloved individuals are manged by psychiatric services 2. The importance of the Supreme Court of Pakistan Judgement in affecting change 3. Highlights on how Queen’s University will enhance forensic psychiatry in Pakistan Methods A literature review and personal networking facilitated the collection of important data in how justice invloved individuals are supported in Pakistan. The author has published and presented to Pakistani psychiatrists and the Pakistani judiciary on this topic. Queen’s University is aiming to implement a 3-year plan to develop an online curriculum and certificate course to help train the trainers. Results In the Pakistan’s most populous province, Punjab, prevalence rates for psychotic illnesses (3.7%), major depression (10%), and personality disorders (65%) among men with higher rates for psychotic disorders (4.0%) and major depression (12%) among women. Conclusions In conclusion there is a dire need to develop forensic psychiatry in Pakistan and other low/middle income countries. Disclosure No significant relationships.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".