Stakeholder preferences for cancer care performance indicators
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to show that performance data use could be promoted with a better understanding of the type of indicators that are important to different stakeholders. This study explored patient, nurse, physician and manager preferences for cancer care quality indicators. DESIGN/METHODOLOGY/APPROACH: Interviews were held with 30 stakeholders between March and June 2004. They were asked to describe how they would use a cancer "report card", and which indicators they would want reported. Transcripts were reviewed using qualitative analysis. FINDINGS: Role (patient, nurse, physician, manager) influenced preferences and perceived use of performance data. Patients and physicians were more skeptical than nurses and managers; patients and managers expressed some preferences distinct from nurses and physicians; and patients and nurses interpreted indicators more broadly than physicians and managers. All groups preferred technical process over outcome or interpersonal process indicators. RESEARCH LIMITATIONS/IMPLICATIONS: Expressed views are not directly applicable beyond this setting, or to the general public but findings are congruent with attitudes to performance data for other conditions, and serve as a conceptual basis for further study. PRACTICAL IMPLICATIONS: Strategies for maximizing the relevance of performance reports might include technical process indicators, selection by multi-stakeholder deliberation, information that facilitates information application and customizable report interfaces. ORIGINALITY/VALUE: Performance data preferences have not been thoroughly examined, particularly in the context of cancer care. Factors were identified that influence stakeholder views of performance data, and this framework could be used to confirm findings among larger and different populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".