Travel for HIV care in England: a choice or a necessity?
Bibliographic record
Abstract
OBJECTIVES: The aims of the study were (1) to measure the distance required to travel, and the distance actually travelled, to HIV services by HIV-infected adults, and (2) to calculate the proportion of patients who travelled beyond local services and identify socio-demographic and clinical predictors of use of non-local services. METHODS: The straight-line distance between a patient's residence and HIV services was determined for HIV-infected patients in England in 2007. 'Local services' were defined as the closest HIV service to a patient's residence and other services within an additional 5 km radius. Multivariable logistic regression was used to identify socio-demographic and clinical predictors of accessing non-local services. RESULTS: In 2007, nearly 57 000 adults with diagnosed HIV infection accessed HIV services in England; 42% lived in the most deprived areas. Overall, 81% of patients lived within 5 km of a service, and 8.7% used their closest HIV service. The median distance to the closest HIV service was 2.5 km [interquartile range (IQR) 1.5-4.2 km] and the median actual distance travelled was 4.8 km (IQR 2.5-9.7 km). A quarter of patients used a 'non-local' service. Patients living in the least deprived areas were twice as likely to use non-local services as those living in the most deprived areas [adjusted odds ratio (AOR) 2.16; 95% confidence interval (CI) 1.98-2.37]. Other predictors for accessing non-local services included living in an urban area (AOR 0.77; 95% CI 0.69-0.85) and being diagnosed more than 12 months (AOR 1.48; 95% CI 1.38-1.59). CONCLUSION: In England, 81% of HIV-infected patients live within 5 km of HIV services and a quarter of HIV-infected adults travel to non-local HIV services. Those living in deprived areas are less likely to travel to non-local services.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".