Determinants of late diagnosis of HIV infection in Spain
Bibliographic record
Abstract
The main goal of this study was to analyse the determinants of late diagnosis of HIV infection. Secondly, we studied the role of the perception of risk and sexual orientation in HIV testing. Twenty-five people with late HIV diagnosis were interviewed. They were contacted through hospitals and non-governmental organizations (NGOs). To design the interview, we integrated the variables considered in the main models of health-related behaviour. We followed a mixed strategy of analysis. Firstly, we carried out the maticanalysis of the interviews, followed by quantitative analysis of the initially qualitative data. The results revealed that the most relevant determinants were the appraisal of the threat of HIV and the low perception of HIV risk. Also, the study found many missed opportunities for diagnosis in health-care setting. Low perception of HIV risk was related to unrealistic optimism, low levels of information about HIV, and the presence of stereotypes about people with HIV. High perception of HIV risk was related to strategies to avoid testing. Homosexuals reported a more positive balance between the benefits of knowing their diagnosis and having the disease. The results provide clues that can guide the design of future strategies to promote early diagnosis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".