Reshaping perioperative nursing practice to get the job done: A constructivist grounded theory study
Bibliographic record
Abstract
An estimated 234 million operations are performed in hospitals each year and complications of surgery are common and often preventable. The rates of complications vary between studies with reports of perioperative death rates of between 0.4 and 0.8 per cent and rates of complications between 3 and 17 per cent. Adverse events can lead to patient disability, death, or increased length of stay, imposing a significant burden on the health care system, patients and their families. Perioperative nurses have a key role in securing patient safety and preventing mistakes and these are recognised as both the nurses’ responsibility and within their locus of control. Research and evidence-based actions to minimise the risk of patient harm inform the standards developed by the Australian College of Perioperative Nurses. These standards are closely aligned with similar standards in the UK, US and Canada and represent the accepted standard of professional practice for perioperative nurses in Australia.
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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 | 0.003 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".