Perceptions of perioperative nursing competence: a cross-country comparison
Bibliographic record
Abstract
BACKGROUND: Throughout many countries, professional bodies rely on yearly self-assessment of competence for ongoing registration; therefore, nursing competence is pivotal to safe clinical practice. Our aim was to describe and compare perioperative nurses' perceptions of competence in four countries, while examining the effect of specialist education and years of experience in the operating room. METHODS: (PPCS-R), was used with a total sample of 768 respondents. We used a factorial design to examine the influence of country, years of experience in the operating room and specialist education on nurses' reported perceived perioperative competence. RESULTS: Regardless of country origin, nurses with specialist qualifications reported higher perceived perioperative competence when compared to nurses without specialist education. However, cross-country differences were dependent on nurses' number of years of experience in the operating room. Nurses from Sweden with 6-10 years of experience in the operating room reported lower perceived perioperative competence when compared to Australian nurses. In comparing nurses with > 10 years of experience, Swedish nurses reported significantly lower perceived perioperative competence when compared to nurses from Australia, Canada and Scotland. CONCLUSION: Researchers need to consider educational level and years of experience in the perioperative context when examining constructs such as competence.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".