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Record W2943730500 · doi:10.1386/jaah.10.1.73_1

An embodied exercise to address HIV- and tuberculosisrelated stigma of healthcare workers in Southern Africa

2019· article· en· W2943730500 on OpenAlexaff
Annalee Yassi, Simphiwe Mabhele, Elizabeth Wilcox, Vivian W. L. Tsang, Karen Lockhart

Bibliographic record

VenueJournal of Applied Arts and Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStigma (botany)MedicineEmbodied cognitionHealth careHuman immunodeficiency virus (HIV)NursingFamily medicinePsychiatryEconomic growth

Abstract

fetched live from OpenAlex

Healthcare workers (HCW) face the risk of occupational exposure to infectious diseases, especially in countries with high burdens of tuberculosis (TB) and human immunodeficiency virus (HIV). Disclosure of TB and/or HIV status is needed to ensure prompt action and treatment, and, in the case of TB, prevent transmission to co-workers and patients. Traditional education and training has had limited impact. It is known that stigma plays a major role in hindering disclosure of HIV status and accessing treatment. Participatory theatre has been used to cultivate communication skills and empathy. We therefore incorporated an embodied stigma exercise into a multi-country collaboration, in which we brought 78 HCWs from seven hospitals to one of three workshops in South Africa, Mozambique and Zimbabwe, respectively. We describe the exercise, highlight the skilled facilitation needed and present results from exit evaluations and interviews with participants. Some changes in attitudes were noted and our observations provide a basis for considering use of embodied methods in efforts to reduce workplace stigma.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.004
Scholarly communication0.0020.001
Open science0.0010.007
Research integrity0.0010.001
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 head0.029
GPT teacher head0.314
Teacher spread0.285 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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