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Record W4396845115 · doi:10.63050/jpps.19.03.176

CLINICAL APPLICATION OF ICD-11 AND ITS UNDERSTANDING AMONG THE PSYCHIATRISTS AND POST GRADUATE PSYCHIATRIC RESIDENTS OF PAKISTAN

2022· article· en· W4396845115 on OpenAlexaff
Muhammad Nasar Sayeed Khan, Mahnoor Irshad, Amina Nasar

Bibliographic record

VenueJournal of Pakistan Psychiatric Society · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychiatryMedicinePsychologyFamily medicineMedical education

Abstract

fetched live from OpenAlex

Objectives: To find out the problems associated with the clinical utilization of ICD-11 by the psychiatrists of Pakistan. To understand the basic description of its utilization in clinical practice. Study design: It is a cross-sectional descriptive study. Place and duration of study: The study was conducted through an online survey which was circulated to the psychiatrists across country. Subjects and methods: A sample size of 130 psychiatrists including the post graduate residents responded to the e-mail and WhatsApp messages. The survey was on Google Docs, and it was an online survey with responses of “yes, no and don't know”. The ethical approval for the survey was taken from Pakistan psychiatric research centre, fountain house Lahore, Pakistan. Results: Out of 130 psychiatrists, 65.4% were the residents who heard about ICD-11 mostly from academic activity conducted by the authors. 48.5% of psychiatrists believe that it is difficult to adjust with the new classification system. Conclusion: ICD-11 is a new classification system. It requires training from the seniors and workshops related to its utility through the online mortality and morbidity statistics by WHO across various countries.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.321
Teacher spread0.304 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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