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Record W4413361727 · doi:10.1111/inm.70121

Understanding the Multi‐Faceted Nature of the Public Stigma of Mental Illness: Insights and Implications From a Structural Violence Perspective

2025· article· en· W4413361727 on OpenAlexaff
Sebastian Gyamfi, Priscilla Boakye, Natalie Giannotti, Edward Cruz

Bibliographic record

VenueInternational Journal of Mental Health Nursing · 2025
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsToronto Metropolitan UniversityUniversity of Windsor
Fundersnot available
KeywordsMental illnessStigma (botany)Mental healthDysfunctional familyPerspective (graphical)PsychologyPublic healthPsychiatrySocial stigmaMedicineCriminologySocial psychologyNursingFamily medicine

Abstract

fetched live from OpenAlex

The societal stigma surrounding mental illness emerges because of dysfunctional social dynamics between patients, their families and the broader community. This phenomenon, referred to as public stigma, manifests through discriminatory actions or the exclusion of individuals within power structures. Public stigma detrimentally affects the well-being and recovery of those grappling with mental illness, impeding their quality of life. This paper discusses public stigma, examining society's negative attitudes toward individuals diagnosed with mental illness, the systemic injustices perpetuating their marginalisation and the ramifications for nursing practice. Ultimately, the authors propose a theoretical framework to educate and inform health professionals across both mental health and general medical domains, as well as policymakers, about the impact of structural violence in perpetuating public stigma and providing recommendations for remedial action.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0050.024
Scholarly communication0.0080.009
Open science0.0010.008
Research integrity0.0030.005
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 head0.055
GPT teacher head0.436
Teacher spread0.381 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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