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Record W6976725155 · doi:10.60692/1fs7b-gm960

Psychiatric treatment as anti-stigma intervention: Objective assessment of stigma by families

2014· article· en· W6976725155 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStigma (botany)Intervention (counseling)Mental illnessPerceptionMental healthPublic healthSocial stigma

Abstract

fetched live from OpenAlex

Psychiatric treatment as anti-stigma intervention: Objective assessment of stigma by familiesBackground: Stigma related to mental illness is linked with suicide, violence, and lack of self-care, and thus should be treated as a clinical condition.For effective intervention, objective information about the impact of stigma is required in order to offer the best client-centered care.Objective: The present study seeks to answer the question of how stigma and discrimination are perceived to be experienced by their patient family members, to determine factors helpful for development of antistigma intervention programs.Materials and Methods: Three hundred family members of patients with schizophrenia provided their perceptions on aspects of stigma including anti-stigma interventions.There were two types of intervention strategies suggested (1) clinical measures and (2) public health measures.The predominant strategy was clinical measures which encompassed areas of availability of treatment, complete treatment, relapse prevention, and early intervention.Results: Furthermore, caregivers' emotional involvement (64.8%) in treatment was seen as an important measure to reduce stigma.No social and public awareness is going to bring change in patients' lives if stigma is not addressed at an individual level in a client-centric manner.Conclusion: The responses of patient relatives clearly bring out this opinion when they suggest potential treatment components as intervention measures.

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)0.001
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 score0.191
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.279
Teacher spread0.254 · 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
Published2014
Admission routes1
Has abstractyes

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