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Record W4220712592 · doi:10.1186/s43058-022-00278-2

Applying implementation science frameworks to identify factors that influence the intention of healthcare providers to offer PrEP care and advocate for PrEP in HIV clinics in Colombia: a cross-sectional study

2022· article· en· W4220712592 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueImplementation Science Communications · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsQueen's University
FundersMinistry of Science and Technology
KeywordsHealth carePre-exposure prophylaxisExploratory factor analysisMedical prescriptionPsychologyPopulationNursingCross-sectional studyMedical educationMedicineFamily medicineHuman immunodeficiency virus (HIV)PsychometricsClinical psychologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Few studies have used implementation science frameworks to identify determinants of PrEP prescription by healthcare providers. In this work, we developed and psychometrically examined a questionnaire using the theoretical domains framework (TDF) and the consolidated framework for implementation research (CFIR). We used this questionnaire to investigate what factors influence the intention of healthcare providers to offer PrEP care and advocate for PrEP. METHODS: We conducted a cross-sectional study in 16 HIV healthcare organizations in Colombia. A 98-item questionnaire was administered online to 129 healthcare professionals. One hundred had complete data for this analysis. We used exploratory factor analysis to assess the psychometric properties of both frameworks, and multinomial regression analysis to evaluate the associations of the frameworks' domains with two outcomes: (1) intention to offer PrEP care and (2) intention to advocate for PrEP impmentation. RESULTS: We found support for nine indices with good internal consistency, reflecting PrEP characteristics, attitudes towards population needs, concerns about the use of PrEP, concerns about the role of the healthcare systems, knowledge, beliefs about capabilities, professional role, social influence, and beliefs about consequences. Notably, only 57% of the participants were likely to have a plan to care for people in PrEP and 66.7% were likely to advocate for PrEP. The perception of the need for PrEP in populations, the value of PrEP as a practice, the influence of colleagues, and seeing PrEP care as a priority was related to being less likely to be unwilling to provide or advocate for PrEP care. CONCLUSION: Our findings suggested the importance of multilevel strategies to increase the provision of PrEP care by healthcare providers including adquisition of new skills, training of PrEP champions, and strength the capacity of the health system.

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.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.177
GPT teacher head0.593
Teacher spread0.416 · 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