Competing Values of Emergency Department Performance: Balancing Multiple Stakeholder Perspectives
Bibliographic record
Abstract
OBJECTIVE: To describe the performance interests of multiple stakeholders associated with the management and delivery of emergency department (ED) care, and to develop a performance framework and set of indicators that reflect these interests. STUDY SETTING: Stakeholders (1,100 physicians, nurses, managers, home care providers, and prehospital care personnel) with responsibility for ED patients in hospitals in the Canadian province of Ontario. STUDY DESIGN: Sixty-two percent of stakeholders responded to a mail survey regarding the importance of 104 potential ED performance indicators. Descriptive and inferential statistics are used to explore the interests of each stakeholder group and to compare interests across the five groups. PRINCIPAL FINDINGS: Emergency department stakeholders are primarily interested in indicators that focus on their role and capacity to provide care. Key differences exist between hospital and nonhospital stakeholders. Physicians mean ratings of the importance on ED performance measures were lower than mean ratings in the other stakeholder groups. CONCLUSIONS: Emergency department performance interests are not homogeneous across stakeholder groups, and evaluating performance from the perspective of any one stakeholder group will result in unbalanced assessments. Community-based stakeholders, a group frequently excluded from commenting on ED performance, provide important insights into ED performance related to the external environment and the broader continuum of 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 | 0.050 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".