Factors affecting GUM clinic attenders decisions and intentions to seek HIV testing
Bibliographic record
Abstract
OBJECTIVES: To evaluate factors that predict HIV testing using the model of health care utilisation as its conceptual framework and to analyse some of the factors that encourage or inhibit seeking an HIV test in this population. METHOD: A cross sectional questionnaire study in two Genito-Urinary Medicine (GUM) clinics in central Scotland. A final sample of 195 represented a 91% response rate. Participants were categorised by their HIV testing status (already tested, planning to be tested, no intention to seek testing). RESULTS: The 'already tested' and 'planning to be tested' groups were combined as there were no significant differences on reported risk behaviours. Analysis therefore compared two groups those 'testing' (n = 66) and 'not testing' (n = 129). 67% of those not tested for HIV reported at least one HIV risk factor. Perceived risk was the strongest predictor of HIV testing using our model. Perception of risk and actual risk were not correlated. Those not seeking testing endorsed less benefits of testing and more denial of the need to be tested. Same day testing and testing without an appointment were endorsed as factors to promote testing. CONCLUSION: To encourage people who have high risk factors to access HIV testing, programmes should: (1) highlight the benefits of testing which would be lost if people do not test, eg. effective drug treatments (2) increase the range of HIV testing services available (eg. same day testing). Furthermore, studies to determine the main predictors of perceived risk are needed if we are to increase testing in relevant populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".