Testing the reliability and accuracy of urgency ratings determined by triage nurses for mental health scenarios, using the Canadian Triage and Acuity Scale
Bibliographic record
Abstract
Over-crowded emergency departments (EDs) are commonplace necessitating triage systems. Although several triage scales exist, the Canadian Triage and Acuity Scale (CTAS) is used across EDs nationally. Interestingly, few mental health scenarios have been included in studies testing the reliability and accuracy of the CTAS. Moreover, EDs are increasingly used by individuals in a mental health crisis. The purpose of this study was to test the inter-rater reliability and accuracy of triage nurses’ assignment of urgency ratings for mental health patient scenarios utilizing current CTAS guidelines. The overall Fleiss kappa for this sample of triage nurses (n=18) was 0.31180 representing only fair agreement while the Kendall’s coefficient was 0.67964 or moderate agreement. Both calculations were statistically significant (p<.0001). Several limitations exist nevertheless, observations from this study specifically, focus on the use of second order modifiers in education of triage nurses, may improve the inter-rater agreement for mental health presentations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".