The Trauma Nurse Coordinator in Australia and New Zealand
Bibliographic record
Abstract
Trauma nurse coordinators (TNCs) are essential to a successful trauma service. This study followed the 2003 Australian and 2007 binational TNC surveys and aimed to identify demographic information, common and differing role components, and professional development opportunities of TNCs. In September 2011, all TNCs in Australia and New Zealand were invited to participate in an electronic survey. Fifty-three surveys (78%) were completed. Compared with that of 2007, the median years of trauma-related nursing experience decreased from 11 to 6 (P < .0001), the proportion of respondents with specialist trauma qualifications increased from 78% to 96% (P = .037), and there was a significant increase in unpaid overtime (P = .023). Nearly all respondents (92%) had attended a conference within the past year; however, one-quarter of them (24.5%) attended on their own time and more than half (53.1%) received no financial assistance for at least one of the conferences they attended. Nearly half of the respondents (46.0%) reported leading research, and two-thirds (66.7%) reported contributing to research projects. Trauma centers should provide TNCs with adequate resources for daily practice, including professional development to prevent burnout, and facilitate effective trauma services.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".