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Record W2782902327 · doi:10.1192/s205647400000177x

Forensic psychiatric service provision in Pakistan and its challenges

2017· article· en· W2782902327 on OpenAlexaff
Tariq Hassan, Asad Tamizuddin Nizami, M. Selim Asmer

Bibliographic record

VenueBJPsych International · 2017
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsQueen's University
FundersKing's College London
KeywordsLegislationMental healthcareMental healthIslamColonialismState (computer science)Forensic psychiatryPolitical sciencePsychiatryMentally illService (business)LawMedicineCriminologyPsychologyMental illnessGeographyBusiness

Abstract

fetched live from OpenAlex

In the Islamic Republic of Pakistan the law relating to people who are mentally ill, until 2001, was set out by the Lunacy Act of 1912, which was inherited from the British colonial occupiers. In 2001 the Mental Health Ordinance 2001 took its place but only for this federal law to be superseded in April 2010 with the 18th constitutional amendment. As part of that amendment, provinces have become responsible for (psychiatric) healthcare, including mental health legislation. Forensic psychiatry is practised in Pakistan but is very much in its infancy; it needs to develop and learn from more experienced countries in Europe and North America. Cultural factors and misconceptions arising from religion can at times contribute to, or create, barriers to the implementation of forensic psychiatric services in Pakistan. This paper reviews the current state of forensic psychiatric services in Pakistan and is intended to open the debate on the challenges ahead.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.068
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.007
Scholarly communication0.0080.005
Open science0.0020.007
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.073
GPT teacher head0.452
Teacher spread0.379 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2017
Admission routes1
Has abstractyes

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