Importance of stratification when measuring quality of care: Results from the Project for an Ontario Women's Health Evidence-Based Report Card (POWER) study
Bibliographic record
Abstract
6573 Background: The goal of the POWER study is to improve health and reduce inequities among women of Ontario using population-based performance measurement. All selected indicators are stratified by sex, age, income and place of residence to determine whether there are disparities in performance associated with these factors. Results pertaining to cancer (CA) indicators are presented. Methods: A modified Delphi approach was used to identify performance measures spanning the disease spectrum from screening through follow-up care and feasible to be evaluated from administrative data. Most recently available data (2003–2005) from the Ontario Cancer Registry, Registered Persons, billing and hospital databases were then used to evaluate the selected measures stratified by above four factors. Results: Twenty-nine cancer-specific indicators were selected by the panel, majority of which related to processes of care: general indicators relevant to multiple cancers (3), screening (5), breast CA (5), lung CA (3), colorectal CA (4), gynecologic CA (4), and end-of-life care (5). Among measures relating to non-sex specific cancers, few sex-related differences in care were identified. Income-based differences in care were most pronounced among the screening indicators with individuals from low-income neighborhoods less likely to receive appropriate screening or follow-up after an abnormal screening test. Among measures relating to treatment, age was the factor most frequently associated with differences in care especially in regards to radiation- or chemotherapy-related processes of care. There were geographic differences in quality of care within the province along the entire trajectory of cancer care from screening through end-of-life care. Conclusions: Among the cancer quality indicators considered, few sex-based differences exist. However, factors such as age, income and where one lives are important predictors of care underscoring the importance of stratification by such factors when evaluating quality of care. Further work is necessary to determine whether these differences in processes of care affect outcomes and how to facilitate access to care by disadvantaged groups. No significant financial relationships to disclose.
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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 | 0.082 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".