Staring, tone of voice, anxiety, mumbling, and pacing in the ED were cues for violence toward nursesCommentary
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it