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Record W2399757696 · doi:10.3233/978-1-61499-415-2-133

Appreciating the Persona Paradox: Lessons from Participatory Design Sessions with HIV+ Gay Men

2014· article· en· W2399757696 on OpenAlexafffund
Jae‐Yung Kwon, Leanne M. Currie

Bibliographic record

VenueStudies in health technology and informatics · 2014
Typearticle
Languageen
FieldComputer Science
TopicPersona Design and Applications
Canadian institutionsUniversity of OttawaUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsPersonaParticipatory designHuman immunodeficiency virus (HIV)Stigma (botany)PsychologySocial psychologyIntervention (counseling)LesbianSociologyGender studiesComputer scienceMedicineEngineeringHuman–computer interaction

Abstract

fetched live from OpenAlex

Eliciting user requirements from HIV-positive gay men who smoke can be challenging. This is because of the complex relationship between social stigma and gender identities (e.g., gay, masculine, HIV+, and smoking status). Inspired to engage HIV-positive gay men in the development of a web-assisted tobacco intervention, we used personas as a main communication tool in our participatory design sessions. Personas are characters created by users that embody part of their own behaviours, thoughts, and motivations. In an apparent paradox, this article is a description of how the use of personas to ensure less realistic self-representation provided an impetus for more self-disclosure. Findings and feedbacks from this study reveal that personas are an effective design tool to engage users in sensitive topics. Implications for future work are also discussed.

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)0.084
metaresearch head score (Gemma)0.074
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 score0.084
Threshold uncertainty score0.444

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0840.074
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0130.016
Scholarly communication0.0090.009
Open science0.0040.013
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.145
GPT teacher head0.380
Teacher spread0.234 · 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

Citations6
Published2014
Admission routes2
Has abstractyes

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