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Record W4401787312 · doi:10.1111/asap.12419

Coping with the stigma of mental illness: An interpretive descriptive study of out‐patients in a public mental health hospital in Ghana

2024· article· en· W4401787312 on OpenAlexaff
Sebastian Gyamfi, Ebenezer Martin‐Yeboah, Mark Fordjour Owusu, Joseph Adu

Bibliographic record

VenueAnalyses of Social Issues and Public Policy · 2024
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsToronto Metropolitan UniversityLondon Health Sciences CentreWestern UniversityUniversity of Windsor
Fundersnot available
KeywordsMental illnessPsychologyMental healthCoping (psychology)Stigma (botany)SecrecyPsychiatryClinical psychologyPublic healthSocial psychologyMedicineNursing

Abstract

fetched live from OpenAlex

Abstract Stigma reduces the status of individuals from full social acceptance, motivating the stigmatized person to find ways to cope with the perceived threat as much as possible. The present study explored the experience of dealing with a mental illness daily within the public space. We applied an interpretive description method using a semi‐structured interview guide to elicit subjective responses from 12 purposefully recruited outpatients. Study participants described various ways through which individuals coped with their illness, including secrecy, avoidance/withdrawal, relaxation techniques, confrontation, ignoring the stigmatizing agent, ingroup comparisons, and engaging in diversion activities. The participants' observations suggest they were unhappy about how society perceived and treated them. To deal with stigma, multifaceted approaches of active engagement with the public, healthcare providers, policymakers, and government are needed to mitigate the phenomenon.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0090.007
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.063
GPT teacher head0.449
Teacher spread0.386 · 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 designQualitative
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

Citations2
Published2024
Admission routes1
Has abstractyes

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