Nurses’ triage assessments were affected by patients’ behaviours and stories and their perceived credibilityCommentary
Bibliographic record
Abstract
B Edwards Dr B Edwards, Bournemouth University, Bournemouth, UK; bedwards@bournemouth.ac.uk How do nurses who perform triage in emergency departments (EDs) initially assess patients? Qualitative study using the grounded theory approach. 2 EDs in the UK. Self-selected sample of 14 nurses who regularly did triage in the ED and had 3–20 years of experience in emergency care. Triage encounters involving patients who were confused, acutely ill, or distressed were excluded. Data were collected through 38 video recordings of triage encounters over 9 months. Nurses watched tapes of their own encounters; tapes were stopped after each of their comments or questions and they were asked to describe their thoughts at that time. Their thoughts were recorded, transcribed, and analysed using the constant comparative method. The main intervening condition during triage was nurses’ appraisal of client credibility . (1) Initial visualising and client credibility . Assessment began before the triage encounter, as soon as nurses saw patients, and was intuitive and subjective: “You do sort of make a mental, quick assessment of the patient as they …
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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 | 0.005 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".