Measures of Quality of Care for People with HIV: A Scoping Review of Performance Indicators for Primary Care
Bibliographic record
Abstract
The healthcare of people with HIV is transitioning from specialty care to the primary healthcare (PHC) system. However, many of the performance indicators used to measure the quality of HIV care pre-date this transition. The goal of this work was to examine how existing HIV care performance indicators measure the comprehensive and longitudinal care offered in a PHC setting. A scoping review consisting of peer-reviewed and grey literature searches was performed. Two reviewers evaluated study eligibility and indicators in documents meeting inclusion criteria were extracted into a database. Indicators were matched to a PHC performance measurement framework to determine their applicability for evaluating quality of care in the PHC setting. The literature search identified 221 publications, of which 47 met inclusion criteria. 1184 indicators were extracted and removal of duplicates left 558 unique indicators. A majority of the 558 indicators fell under the 'secondary prevention' (12%) and 'care of chronic conditions' (33%) domains when indicators were matched to the PHC performance framework. Despite the imbalance, nearly all performance domains in the PHC framework were populated by at least one indicator with significant concentrations in domains such as patient-provider relationship, patient satisfaction, population and community characteristics, and access to care. Existing performance frameworks for the care of people with HIV provide a comprehensive set of indicators that align well with a PHC performance framework. Nonetheless, some important elements of care, such as patient-reported outcomes, are poorly covered by existing indicators. Advancing our understanding of how the experience of care for people with HIV is impacted by changes in health services delivery, specifically more care within the PHC system, will require performance indicators to capture this aspect of HIV care.
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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 | 0.083 | 0.283 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.042 | 0.046 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".