Silence in court: the devaluation of the stories of nurses in the narratives of health law
Bibliographic record
Abstract
Silence in court: the devaluation of the stories of nurses in the narratives of health law This paper sets out to address one of the major findings from an extensive analysis of case law involving nurses from 1904 to 1999. The 180 cases were collected from the civil, coronial, professional and industrial jurisdictions of Australia, Canada and the UK. It specifically examines the way in which nurses’ voices and experiences are excluded from legislation and case law, and the resultant effect which this has on the status of the nurse in clinical and healthcare decision‐making. It addresses two of the many issues which emerged from the research, but which are particularly problematic for nurses. The first is that doctors are not necessarily required to heed the clinical concerns of nurses, and the second is that nurses are not necessarily expected to intervene to prevent imminent harm to the patient. This paper also suggests some possible solutions to these issues.
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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 | 0.021 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.077 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".