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Record W3196771113 · doi:10.1155/2021/4789971

Scales to Assess Knowledge, Motivation, and Self-Efficacy for HIV PrEP in Colombian MSM: PrEP-COL Study

2021· article· en· W3196771113 on OpenAlexaff
Héctor Fabio Mueses-Marín, Beatriz Alvarado, Julián Andrés Torres-Isasiga, Pilar Camargo‐Plazas, María Camila Bolívar-Rocha, Ximena Galindo-Orrego, Jorge Martínez-Cajas

Bibliographic record

VenueAIDS Research and Treatment · 2021
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsQueen's University
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)Construct validityMen who have sex with menConfirmatory factor analysisScale (ratio)Exploratory factor analysisConstruct (python library)Stigma (botany)PsychologyTest (biology)Social psychologyRelevance (law)Self-efficacySample (material)MedicineClinical psychologyPsychometricsStructural equation modelingStatisticsFamily medicineComputer scienceMathematics

Abstract

fetched live from OpenAlex

OBJECTIVE: We evaluated the construct validity Spanish version of knowledge, stigma, norms, and self-efficacy scales regarding PrEP in MSM. METHODS: Sample of 287 MSM. Exploratory confirmatory factor analysis and item response theory were used to validate the constructs. Correlations and confidence interval-based estimation of relevance analyses were conducted to correlate the scales with willingness and intention to use PrEP. RESULTS: Attitude, stigma, and descriptive and subjective norms scales showed good construct validity and were related to intention and willingness to use PrEP. However, the knowledge scale and self-efficacy scales require further refinement. CONCLUSIONS: The study provides useful information for assessing information, motivation, and self-efficacy related to PrEP use. Our results could be used to test the scales and the theoretical model in other contexts to confirm their usefulness.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.134
GPT teacher head0.459
Teacher spread0.326 · 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 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

Citations21
Published2021
Admission routes1
Has abstractyes

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