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Record W4407570588 · doi:10.29173/spectrum244

Stigma Cultures in Healthcare Scale – Qualitative Findings in an Emergency Department Setting

2025· article· en· W4407570588 on OpenAlexaffvenueabout
Sarah Sass, Jennifer Smith, Jacqueline Smith, Sarah Horn, Stephanie Knaak

Bibliographic record

VenueSpectrum · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEmergency departmentScale (ratio)Health careQualitative researchMedical emergencyPsychologyNursingMedicineSociologyPolitical scienceGeography

Abstract

fetched live from OpenAlex

One in five Canadians will experience a mental illness. Stigma poses a significant barrier for those with mental illness trying to access treatment. The Exploring Mental health Barriers in Emergency Rooms Study (EMBER) study aims to better understand stigma experienced by those with mental health and addiction concerns in emergency department (ED) settings. For this stage of the study, participants were asked to complete a survey detailing their visit to an ED in a hospital in Southern Alberta. Two scales were used to measure the presence of structural stigma in the ED: the Stigma Cultures in Healthcare Scale (SCHS) and an adapted version of the Patient Experiences of Mental Healthcare (PEMHC) scale. Results showed differential treatment experienced by those with mental health concerns as well as structural changes that could be made to ameliorate the experience of patients with mental health concerns.

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)0.007
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 score0.274
Threshold uncertainty score0.544

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0120.006
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
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 head0.021
GPT teacher head0.411
Teacher spread0.390 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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