Integrating equity indicators for hospital reporting metrics
Bibliographic record
Abstract
Disparities in healthcare delivery and design are deeply-rooted within healthcare systems globally. Many researchers have developed methods to measure inequity; however, there currently exists no accepted measurement approach implemented consistently across health systems. We applied the model-based Relative Index of Inequality (RII) as a measure of inequity at one of Canada's largest health systems, Trillium Health Partners, across two service types: planned and outpatient. Our RII estimates suggest that the lowest-SES individuals received planned and outpatient services at rates 2.4 times and 2.5 times lower than the highest-SES individuals, respectively. Across both service types, the largest disparity was for breast cancer screening, where patients from the lowest-SES neighbourhoods were 5.4 times less likely to use this service at THP. These findings further underscore the importance of consistently measuring and monitoring inequities to develop effective strategies to address the health needs of patients from lower SES neighbourhoods. The approach used within this study should be considered for widespread integration into health system reporting metrics.
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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 | 0.096 | 0.319 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".