Identifying population-level indicators to measure the quality of cancer care for women
Bibliographic record
Abstract
OBJECTIVE: Although there is interest in measuring the quality of cancer care, there has been limited effort to evaluate quality for specific subpopulations such as women or to examine differences in performance among women associated with sociodemographic characteristics. The objective of this study was to identify a comprehensive set of quality indicators for evaluation of the quality of cancer care received by women using administrative data. DESIGN: A conceptual measurement framework developed by the study investigators was used to guide literature review to identify existing quality indicators. The list of potential indicators from the literature was first reviewed by the study investigators with respect to importance and feasibility to determine a set of indicators to present to an expert panel who used a modified Delphi process to select indicators for inclusion using predetermined explicit criteria. SETTING: The Project for an Ontario Women's Health Evidence-Based Report Card. PARTICIPANTS: A multidisciplinary expert panel consisting of clinicians, researchers and administrators with expertise in cancer, quality of care and/or health services research. MAIN OUTCOME MEASURE: Set of quality indicators evaluable from administrative data. RESULTS: The initial literature search identified 427 indicators, of which 46 were rated as important and feasible by the study investigators. Following two rounds of ratings and an in-person meeting, the expert panel recommended 31 indicators for inclusion in the final set spanning the following areas: general indicators (three indicators), cancer screening (six), colorectal cancer (four), lung cancer (three), breast cancer (five), gynecologic cancers (five), and end-of-life care (five). CONCLUSIONS: A comprehensive set of 31 indicators was identified to evaluate the quality of cancer care received by women that also allows assessment of gender and socioeconomic disparities in cancer 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 | 0.053 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".