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Record W2196111747 · doi:10.1586/14760584.2016.1118349

Taking culture seriously in biomedical HIV prevention trials: a meta-synthesis of qualitative studies

2015· review· en· W2196111747 on OpenAlexafffund
Clara Rubincam, Ashley Lacombe‐Duncan, Peter A. Newman

Bibliographic record

VenueExpert Review of Vaccines · 2015
Typereview
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsOperationalizationSociocultural evolutionQualitative researchSystematic reviewPsychologySociologyMEDLINESocial sciencePolitical scienceEpistemology

Abstract

fetched live from OpenAlex

A substantial gap exists between widespread acknowledgement of the importance of incorporating cultural sensitivity in biomedical HIV prevention trials and empirical evidence to guide the operationalization of cultural sensitivity in these trials. We conducted a systematic literature search and qualitative meta-synthesis to explore how culture is conceptualized and operationalized in global biomedical HIV prevention trials. Across 29 studies, the majority (n = 17) were conducted in resource-limited settings. We identified four overarching themes: (1) semantic cultural sensitivity - challenges in communicating scientific terminology into local vernaculars; (2) instrumental cultural sensitivity - understanding historical experiences to guide tailoring of trial activities; (3) budgetary, logistical, and personnel implications of operationalizing cultural sensitivity; and (4) culture as an asset. Future investigations should address how sociocultural considerations are operationalized across the spectrum of trial preparedness, implementation, and dissemination in particular sociocultural contexts, including intervention studies and evaluations of the effectiveness of methods used to operationalize culturally sensitive practices.

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)0.064
metaresearch head score (Gemma)0.407
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.535
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0640.407
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0190.004
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.898
GPT teacher head0.753
Teacher spread0.145 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

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
Published2015
Admission routes2
Has abstractyes

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