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Record W3216440414 · doi:10.15173/ijrr.v4i2.4622

Forensic Psychiatry in Pakistan: An Update

2021· article· en· W3216440414 on OpenAlexaff
Wajahat Ali Malik, Cameron Arnold, Ahila Vithiananthan, Tariq Hassan

Bibliographic record

VenueInternational Journal of Risk and Recovery · 2021
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsQueen's UniversityUniversity of Toronto
Fundersnot available
KeywordsSubspecialtyForensic psychiatryMental healthPsychiatryGovernment (linguistics)Criminal justiceMedicineLegislationPolitical scienceLaw

Abstract

fetched live from OpenAlex

Pakistan is a lower-middle income country in South Asia where forensic psychiatry is often not recognized as a distinct subspecialty of psychiatry. Although evolution toward this direction has begun, more development in this field is needed. Before Pakistan’s Mental Health Ordinance of 2001, much of the mental health legislation and intitutional infrastructure pertaining to the mentally ill offender can be traced back to the Indian Lunacy Act of 1912. The past two decades have witnessed important legal developments in the role of psychiatry in Pakistan’s criminal justice system. This has been seen through the devolution of health-care provision and by an extension of psychiatric service provision from the federation (federal government) to the four provinces. Despite the sparse resources allocated to psychiatry, competent yet scarce psychiatry residents are graduating from Pakistan’s accredited residency programs with an interest in forensic psychiatry. The objective of this article is to reflect on the past, while examining the current state of existing forensic mental health in Pakistan. This article will also address the future trajectory of forensic psychiatry in Pakistan and supports the establishment of forensic psychiatry as a subspecialty in Pakistan.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.375
Teacher spread0.361 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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