Staring, tone of voice, anxiety, mumbling, and pacing in the ED were cues for violence toward nursesCommentary
Bibliographic record
Abstract
L Luck Correspondence to: Ms L Luck, James Cook University, Queensland, Australia; lauretta.luck@jcu.edu.au Which components of observable behaviour in patients, their families, and friends indicate a potential for violence toward nurses in the emergency department (ED)? Instrumental case study using a concurrent mixed-method approach. 33-bed ED in a public hospital in Australia. 20 ED nurses (90% women). Phase 1 comprised thematic analysis of 50 hours of unstructured participant observation, an unstructured interview with 3 nurses, and researcher journaling. In Phase 2, these findings provided items for a structured observation tool to collect quantitative data and informed the content for the qualitative interview guide. Qualitative data collection comprised 290 hours of participant observation on 51 separate occasions over 5 months (16 violent events were observed); 16 recorded, semi-structured, 45–60 minute interviews with nurses; 13 recorded, informal, and unstructured 30–40 minute field interviews, some of which occurred after a violent event was witnessed; review of organisational documents; and research journaling. Violent behaviour was defined as physical or non-physical (eg, abusive or …
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 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".