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
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".