Quality indicators for end-of-life breast cancer care: Is there agreement between stakeholder groups?
Bibliographic record
Abstract
16034 Background: Quality indicators (QIs) are tools designed to measure quality of care and help enhance quality through identifying areas needing improvement. Breast cancer offers a disease model to examine QIs for end-of-life (EOL) care. The objective of this study was to assess agreement among stakeholder groups in two Canadian provinces on QIs for EOL breast cancer. Methods: A qualitative study design using a modified Delphi method and focus groups at each study site. After a literature review, an expert panel identified 19 QIs that were potentially measurable using administrative data. The Delphi panels and focus group sessions incorporated the 19 QIs as discussion topics in Halifax, NS and Ottawa, Ont. The Delphi panels involved a multidisciplinary group of oncology health care professionals. Separate focus groups were conducted with women with metastatic breast cancer and bereaved caregivers. All group sessions were audio-taped, transcribed verbatim, audited and a thematic analysis was conducted. Results: A total of 23 health care professionals, 16 patients, 7 bereaved caregivers participated in the study. Participants attended only one group discussion, depending on group assigned. There was good agreement on QIs among patient and caregiver groups in both cities. The need for effective communication was identified as a major theme. The Delphi process yielded overall moderate agreement with QIs among health care professionals. Conclusion: Aspects of quality EOL care important to stakeholders may not be measurable from administrative data. Results from the Delphi panels indicate that patient preferences and differences in health care delivery between different jurisdictions modulated extent of agreement with QIs. 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 | 0.109 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".