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Record W2603957750 · doi:10.1177/1049732317697947

“Like Holding an Umbrella Before It Rains”: Acceptability of Future Rectal Microbicides Among Men Who Have Sex With Men in India—A Modified Technology Acceptance Model

2017· article· en· W2603957750 on OpenAlexafffund
Venkatesan Chakrapani, Peter A. Newman, Murali Shunmugam, Shruta Mengle, Ruban Nelson, Clara Rubincam, Pushpesh Kumar

Bibliographic record

VenueQualitative Health Research · 2017
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of TorontoCanada Research Chairs
KeywordsMen who have sex with menMicrobicides for sexually transmitted diseasesFocus groupCondomPsychological interventionMicrobicideTechnology acceptance modelSocial psychologyPsychologyMedicineMedical educationHuman immunodeficiency virus (HIV)Family medicineNursingUsabilityPopulationEnvironmental healthBusinessMarketingComputer science

Abstract

fetched live from OpenAlex

Topical rectal microbicides (RMs) are a new prevention technology in development that aims to reduce the risk of HIV acquisition from anal sex. We examined RM acceptability among men who have sex with men (MSM) in India. We conducted a qualitative exploratory study guided by a modified Technology Acceptance Model, with 10 focus groups ( n = 61) of MSM and 10 key informant interviews. Data were explored using framework analysis. RM acceptability was influenced by technological contexts: perceived usefulness of RMs, perceived ease of use of RM and applicator, and habits around condom and lubricant use; individual and interpersonal contexts: perceived relevance and preferences for product formulation and dosing frequency; and MSM community/social contexts: perceived social approval, RM-related stigma, social support. Implementation of RMs for MSM in India may be supported by multi-level interventions that engage community-based organizations in destigmatizing and distributing RMs, ideally gel-based products that enable on-demand use before sex.

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)0.012
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.232
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.169
GPT teacher head0.528
Teacher spread0.359 · 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 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

Citations17
Published2017
Admission routes2
Has abstractyes

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