MétaCan
Menu
Back to cohort
Record W4405815166 · doi:10.1002/jcop.23164

Impact of the Stigma of Mental Illness: A Descriptive Exploratory Study of Outpatients in a Public Mental Health Hospital in Ghana

2024· article· en· W4405815166 on OpenAlexaff
Sebastian Gyamfi, Mark Fordjour Owusu, Joseph Adu, Ebenezer Martin‐Yeboah

Bibliographic record

VenueJournal of Community Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsToronto Metropolitan UniversityWestern UniversityUniversity of Windsor
Fundersnot available
KeywordsMental illnessStigma (botany)Mental healthExploratory researchPsychologyPsychiatrySocial stigmaClinical psychologySocial exclusionDescriptive researchSocial distanceMedicineDiseaseFamily medicine

Abstract

fetched live from OpenAlex

Despite ongoing efforts, persons with mental illness (PWMI) continue to experience stigma and discrimination and with profound negative outcomes. This study examined the psychological and social impact of the stigma attached to mental illness as experienced by out-patients at a public mental health facility. We applied a descriptive exploratory method using a semi-structured interview guide to elicit subjective responses from 12 Outpatient Department members. Study participants described various ways the stigma of mental illness impacted them within the social space. Overall, five (5) themes emerged. These include devaluing, losing their partners, social exclusion, unemployment, and loss of self-esteem. Participants' account of their experiences with stigma so far depicts stigma as an everyday occurrence that adversely impacts their social standing. To effectively address stigma requires intentional efforts to bridge the gap created by deliberate acts of discrimination and lack of support for PWMI within our social framework.

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)0.002
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.112
GPT teacher head0.449
Teacher spread0.337 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Community PsychologySame topicMental Health Treatment and AccessFrench-language works237,207