Barriers and facilitators to HIV pre-exposure prophylaxis uptake among transgender women in Colombia: A qualitative analysis using the COM-B model
Bibliographic record
Abstract
Transgender women [TGW] in Colombia are disproportionately affected by HIV due to their low sociodemographic conditions, varied risk behaviours, difficulty accessing health services, and discrimination. Offering pre-exposure prophylaxis [PrEP] as part of a combination of prevention strategies is an appropriate option for this population to reduce their risk of HIV infection. However, little is known about how to implement a PrEP program for TGW in Colombia. Between June and October 2020, we conducted individual interviews with 16 TGW from four different cities in Colombia. The interviews assessed contextual influences, knowledge, skills, perceptions, and beliefs. We used qualitative thematic analysis to identify themes and the Capability, Opportunity, Motivation, and Behavior framework to further delineate barriers and possible interventions. After delineating the main themes across the three subdomains of the model, nine barriers were identified: one related to capability, knowledge, and perception of PrEP; six related to opportunity, which includes, family relations, sexual work environment, stable partner relations, interactions with healthcare workers, health service provision, and community interactions and opportunities; and two related to motivation, mental health, and concerns about medication side effects. Mapping barriers with interventions generated the following intervention functions: education, training, enablement, and environmental structure; and the following policy functions: communication/marketing, legislation, and changes in service provision. Examples of possible interventions are presented and discussed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".