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Record W2087723354 · doi:10.5539/ass.v8n9p74

Mental Health Literacy among Family Caregivers of Schizophrenia Patients

2012· article· en· W2087723354 on OpenAlexvenueno aff
M. S. Mohamad, PA Zabidah, I. Fauziah, Norulhuda Sarnon

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsnot available
Fundersnot available
KeywordsMental health literacyMental illnessMental healthEthnic groupPsychologyStigma (botany)LiteracyFamily caregiversCoping (psychology)Middle Eastern Mental Health Issues & SyndromesPsychiatryHealth literacyHelp-seekingClinical psychologyMedicineNursingHealth care

Abstract

fetched live from OpenAlex

The benefits of public knowledge towards physical health are widely accepted but the area of mental health literacy remains undervalued and relatively neglected. The study aimed to identify caregivers’ mental health literacy in Malaysia. There were 154 family caregivers participated in the face-to-face semi-structured interview regarding their personal caring experiences. This study found that majority of the caregivers was women aged less than 60 years. Most of the caregivers have some understanding about their relatives’ mental illness. More than half of the participants found that the doctors were considered as their primary source of information about mental health. Consistent with previous literature in Malaysia, most of the caregivers used religious and traditional coping mechanism in their help-seeking processes. Each ethnic group had their own strong cultural beliefs about mental illness. The implications for mental health services are that many of the caregivers need help to educate their family members about mental illness. While this study emphasized on the family members who should be targeted to improve mental health literacy it also become significant to the public to reduce stigma towards the person with mental illness and their family.

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 score0.730
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.366
Teacher spread0.347 · 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

Citations24
Published2012
Admission routes1
Has abstractyes

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