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Record W2014394526 · doi:10.3109/09638237.2013.775407

“Integrating Kuwait’s Mental Health System to end stigma: a call to action”

2014· editorial· en· W2014394526 on OpenAlexaff
Hind Almazeedi, Mohammad Alsuwaidan

Bibliographic record

VenueJournal of Mental Health · 2014
Typeeditorial
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsStigma (botany)Mental healthDilemmaPsychiatryMedicineCall to actionMental illnessNursingHealth carePsychologyEconomic growthBusiness

Abstract

fetched live from OpenAlex

Despite the global prevalence of mental illness and its negative effects on the economy in terms of healthcare spending, many affected individuals do not receive timely or adequate treatment due to stigmatization of such disorders in their communities. Being labeled as mentally ill can have detrimental consequences in several cultures. In Kuwait, the stigma associated with visiting the country's main provider of mental health services, the Psychological Medicine Hospital, is an obstacle for many seeking professional help for mental health. Cultural acceptance of visiting the local primary care clinic, however, allows frequent contact with primary care physicians who often find themselves frustrated at their inability to provide psychiatric services because it is not part of their training. The refusal of the patient to be referred to a stigmatized institution further increases the challenges of treating such patients for these physicians. The integration of mental health care into general health services is a concept encouraged by the World Health Organization's 2001 World Health Report and should be considered in order to overcome this dilemma. Such integrated care would serve as a cost-effective solution to facilitating the treatment of these individuals and reducing the stigma associated with mental disorders through education.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.060
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.035
GPT teacher head0.428
Teacher spread0.393 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations27
Published2014
Admission routes1
Has abstractyes

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