Correlations among measures of quality in HIV care in the United States: cross sectional study
Bibliographic record
Abstract
OBJECTIVE: To determine whether a selected set of indicators can represent a single overall quality construct. DESIGN: Cross sectional study of data abstracted during an evaluation of an initiative to improve quality of care for people with HIV. SETTING: 69 sites in 30 states. DATA SOURCES: Medical records of 9020 patients. MAIN OUTCOME MEASURES: Adjusted performance rates at site level for eight measures of quality of care specific to HIV and a site level summary performance score (the number of measures for which the site was in the top quarter of the distribution). RESULTS: Of 28 site level correlations between measures, two were greater than 0.40, two were between 0.30 and 0.39, four were between 0.20 and 0.29, and the 20 remaining were all less than 0.20. One site was in the top quarter for seven measures, but no sites were in the top quarter for six or eight of the measures. Across the eight quality measures, sites were in the top quarter no more often than predicted by a chance (binomial) distribution. CONCLUSIONS: The quality suggested by one measured indicator cannot necessarily be generalised to unmeasured indicators, even if this might be expected for clinical or other reasons.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".