Equity Landscape in Healthcare Quality: A Mixed-Methods Study of Efforts Within Surgical Quality Programs
Bibliographic record
Abstract
BACKGROUND: Addressing equity in healthcare is necessary to improve population health outcomes. In doing so, a requisite level of foundational resources, organization, and processes is needed. Although increasing attention is being devoted to addressing health inequity, the current landscape supporting these efforts remains unknown. We sought to evaluate the presence of frontline resources, organization, and processes for support of health equity efforts in hospitals participating in American College of Surgeons (ACS) quality programs. STUDY DESIGN: Using a mixed-methods design of online surveys and semistructured interviews, we evaluated hospitals with ACS quality programs. Descriptive analytics were applied to survey results; interview transcripts were coded using an inductive approach. Data and methods were triangulated to report key findings. RESULTS: Of the 44 programs invited, 36% completed the survey. Five site program leaders were interviewed. All program leaders reported having a strategy at the institution level for supporting equity efforts and having processes in place for reporting instances of discrimination, mistreatment, or harassment. Survey results demonstrated deficient workforce capacity, lack of engagement, and insufficient organization-negatively impacting efforts. The key themes from interviews were (1) implementation occurred primarily and superficially at the institutional level; (2) barriers to implementation included preemptive structure, lack of prioritization, and insufficient disparity data; and (3) opportunities included enhancing leadership and staff buy-in, increasing available resources, developing health equity champions, and defining clear strategies. CONCLUSIONS: Efforts aimed at achieving health equity exist but lack the necessary infrastructure, organization, and processes to support effective frontline practices. The findings from this study support consideration of standards development targeting problems and opportunities at both the institutional and program levels for advancing equity in quality improvement efforts.
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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 | 0.039 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".