Perioperative nurses' perceptions of competence: implications for migration.
Bibliographic record
Abstract
BACKGROUND: Nurses' recognition of their own level of skills and abilities (ie perceived competence) is a prerequisite for ensuring they can practice in a safe manner. The demand for competence, in the operating room, may vary between clinical environments. It is, however, unclear what competency levels migrating nurses need in order to be deemed safe. AIM: This paper describes Canadian and Australian nurses' levels of perceived perioperative competence and discusses these results in the context of nurse migration. METHOD: A survey was distributed to operating room nurses in six hospital sites (three in Canada and three in Australia). Perioperative competence was measured with a 40-item self-report survey which consisted of six domain subscales: foundational knowledge and skills; leadership; collaboration; proficiency; empathy; and professional development. Non-parametric tests were used to describe differences between groups based on country of origin, years of experience and specialty qualifications. RESULTS: Canadian and Australian nurses reported their overall competency levels as high across all domains. Significant differences were found, between countries, in three of the six competency domains; foundational knowledge and skills (p < .001), collegiality (p = .023), and empathy (p < .0001). CONCLUSIONS: Describing perioperative competence cross-nationally represents the first step in generating international dialogue around educational preparation for migrating nurses. The increasing global mobility of nurses makes it imperative to further standardise, with an international perspective, knowledge and practice expectations in perioperative settings.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".