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Record W2888467931 · doi:10.1177/2158244018794800

From “ <i>Their Stigma”</i> to “ <i>My Stigma”</i> : An Examination of the “ <i>Skul Konekt”</i> Project Among Adolescents in North-Central Region of Nigeria

2018· article· en· W2888467931 on OpenAlexaff
Taiwo Afolabi

Bibliographic record

VenueSAGE Open · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsStigma (botany)Citizen journalismParticipatory action researchPsychologyIntervention (counseling)Focus groupSociologyPublic relationsMedical educationPolitical scienceMedicine

Abstract

fetched live from OpenAlex

This article investigates theater techniques employed in addressing self-stigmatization in Skul Konekt project, an “ anti- self- stigma” HIV/AIDS theater intervention in Nasarawa state, Nigeria. The project toured secondary schools in Nasarawa State to create awareness on the negative effect of self-stigmatization in people living with HIV/AIDS (PLHIV). This study answers the research question: What theater techniques were employed in the Skul Konekt project and how did these strategies address the theme of self-stigmatization in PLHIV? My arguments are supported with the play text, titled, Talk to Me, and reflections from the playwright and selected actors. My reflection as a participant–observer in the project; comments from government officials, students, parents, and teachers during talkback session; and evaluation sessions from the project coordinator form part of the analysis of the impact of the theater techniques. Findings show that many intervention campaigns focus on stigmatization rather than self-stigmatization because such campaigns are designed through a top-down participatory approach without much consultation with the people. It concludes by discussing the implications of the findings for participatory strategies that can foster open communication, collaboration, and a people-led participatory approach relevant in achieving UNAIDS 90-90-90 objectives.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.323
Teacher spread0.288 · 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 teacher head, 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
Published2018
Admission routes1
Has abstractyes

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