Using a health equity lens to measure patient experiences of care in diverse healthcare settings
Bibliographic record
Abstract
Abstract People who are structurally disadvantaged and marginalized often report poor health care experiences due to intersecting forms of stigma and discrimination. There are many measures of patient experiences of care, however, few are designed to measure equity-oriented care. In alignment with ongoing calls to integrate actions in support of health equity, we report on the development and testing of patient experiences of care measures that explicitly use a health equity and intersectional lens. Our analysis focuses on two different equity-oriented health care scales. The first was piloted in a primary health care setting, where patients have an ongoing relationship with providers over time. The second was piloted in an emergency department, where care is provided on an episodic basis. Item Response Theory was used to develop the scales and evaluate their psychometric properties. The primary health care scale, tested with a cohort of 567 patients, showed that providing more equity-oriented health care predicted improvements in important patient self-report health outcomes over time. The episodic scale, tested in an emergency department setting with 284 patients, showed evidence of concurrent validity, based on a high correlation with quality of care. Both scales are brief, easy-to-administer self-report measures that can support organizations to monitor quality of care using an equity lens. The availability of both scales enhances the possibility of measuring equity-oriented health care in diverse contexts. Both scales can shed light on experiences of care using an intersectional lens and equity-oriented lens, providing a nuanced understanding of quality of 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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".