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Record W2057350900 · doi:10.1177/1049732315581614

Personas to Guide Understanding Traditions of Gay Men Living With HIV Who Smoke

2015· article· en· W2057350900 on OpenAlexafffund
J. Craig Phillips, Derek Rowsell, Jack Boomer, Jae‐Yung Kwon, Leanne M. Currie

Bibliographic record

VenueQualitative Health Research · 2015
Typearticle
Languageen
FieldComputer Science
TopicPersona Design and Applications
Canadian institutionsUniversity of British ColumbiaBritish Columbia Lung AssociationUniversity of Ottawa
FundersCanadian Institutes of Health ResearchCenters for Disease Control and Prevention
KeywordsThematic analysisMainstreamEthnographyPhotovoicePersonaParticipatory action researchSociologyPsychologyGender studiesSocial psychologyQualitative researchAnthropology

Abstract

fetched live from OpenAlex

Gay men living with HIV (GMLWH) who smoke are less responsive to generalized smoking reduction and cessation (SRC) programs than heterosexual persons. This study explored perspectives of GMLWH during the design of a web-based SRC intervention. Participatory design techniques were used to guide the creation of personas that are composite representations of a person who would use the web-based SRC intervention. Researcher-participants (n = 13) created all data. Data analysis involved thematic coding drawing from an ethnographic perspective. Thematic analysis revealed seven intersecting themes related to SRC among participants, and an overarching theme navigating life. Concepts drawn from our ethnographic approach highlight cultural differences between GMLWH and mainstream society. Personas offer a mechanism for interpreting experiences and traditions of GMLWH. SRC interventions with GMLWH must address their social realities that include tools for navigating life, disease, and social identity.

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)0.029
metaresearch head score (Gemma)0.026
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 score0.029
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0070.012
Scholarly communication0.0060.007
Open science0.0020.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.763
GPT teacher head0.576
Teacher spread0.186 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

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