Interrater Reliability of a Tool Measuring the Quality of Nursing Triage in the Emergency Department
Bibliographic record
Abstract
Introduction: Triage plays an essential role in the Emergency Department (ED), helping maintain a safe patient flow. Although assessing the quality of the triage process is crucial, to date, there has been no metrological testing of a tool measuring the quality of nursing triage. Objective: This study aimed to assess the interrater reliability of the Audit Triage Tool (ATT) in Quebec, Canada. Methods: This retrospective cohort study took place in a regional ED. Fifty triages were selected using a systematic random sampling technique with quotas of 10 triages grouped under 5 chief complaints: chest pain, abdominal pain, neurological problems, major blunt trauma and fever. A total of 4 auditors individually applied the 49 criteria of the ATT to 50 triages. The interrater reliability was measured with the intraclass correlation coefficient (ICC), percentage of unanimity (PU) and percentage of agreement (PA). Results: Based on the ICC, 33/49 criteria showed fair (ICC 0.60, comparatively to only 2/26 implicit criteria. Discussion and conclusion: Findings showed that a quarter of the ATT criteria had poor interrater reliability according to various statistical tests. Solutions to improve the reliability of the ATT, mostly regarding the implicit criteria, are needed. Finally, future methodological research on triage quality assessment should focus on a thorough validation of the ATT.
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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 | 0.059 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".