Monitoring equity in the delivery of health services: a Delphi process to select healthcare equity indicators
Bibliographic record
Abstract
AIMS OF THE STUDY: Health equity is a key component of quality of care and an objective for a growing number of quality improvement projects for deontological, ethical, public health and economic reasons. To monitor equity in the delivery of health services in Switzerland, there is a need to implement valid, measurable and actionable equity indicators, along with vulnerability stratifiers such as migrant status, which could lead to differences in quality of care. The aim of this study was to develop a set of healthcare equity indicators and stratifiers targeting inpatient and outpatient populations and to test their feasibility. METHODS: A scoping literature review and inputs from a national interprofessional expert taskforce provided a set of indicators and vulnerability stratifiers. The most valid and measurable indicators and stratifiers were retained using a Delphi process. They were then operationalised, and their implementation tested in three Swiss hospitals from the three language regions. RESULTS: A taskforce of 18 experts, including a patient representative, selected 11 indicators that evaluate structures, processes and outcomes, and five vulnerability stratifiers. Although most indicators and stratifiers could be implemented in all three hospitals, data availability was limited for some variables, including patient satisfaction and access to interpreters for foreign-language patients. CONCLUSIONS: The equity indicators and stratifiers identified by this two-stage process have content validity, wide patient coverage and are focused on inequities in the healthcare system that are actionable through improvement projects. Both the indicators and the project methodology could be replicated in institutions aiming for more equitable 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 | 0.263 | 0.200 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".