Patient Self-Reported Health, Clinical Quality, and Patient Satisfaction in English Primary Care: Practice-Level Longitudinal Observational Study
Bibliographic record
Abstract
OBJECTIVES: To examine the association of self-reported health of patients in general practices, as measured by the EQ-5D-5L, with practice clinical quality and patient-reported satisfaction with accessibility and consultations. METHODS: We used data from the General Practitioner (GP) Patient Survey to construct a practice-level EQ-5D-5L index as the health outcome. Key explanatories were patient-reported measures of satisfaction with access and consultations (also derived from the GP Patient Survey) and clinical quality measured by the achievement of clinical quality indicators reported in the Quality and Outcomes Framework. We estimated practice-level linear panel data models with random and fixed practice effects and practice and patient covariates using 2012/13 to 2016/17 data on more than 7500 English general practices. RESULTS: Bivariate correlations of the EQ-5D-5L index with quality measures were 0.048 for clinical quality, 0.071 for satisfaction with access, and 0.107 for satisfaction with GP consultations (all with P<.001). In both fixed effects regressions, which allow for unobserved time invariant practice characteristics, and random effects regressions which do not, the EQ-5D-5L index was positively associated with 1-year lags of patient satisfaction with access and GP consultations. Patient-reported health was positively associated with clinical quality in the fixed effects regressions. The implied effects were small in all cases. CONCLUSION: Practice-level EQ-5D-5L is positively associated with clinical quality and with 1-year lags of patient-reported satisfaction with access and GP consultations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".