Factors that predict evidence use by Australian perioperative nurses
Bibliographic record
Abstract
Evidence-based practice has been demonstrated to positively impact patient outcomes; unfortunately, there are many factors that hinder the use of research evidence by healthcare clinicians. Our previous study reported a multisite survey assessing Australian perioperative nurses knowledge, practice, attitude, and perceived barriers to evidence use. This subsequent analysis used univariate and multivariate binary logistic regression with odds ratios (OR) and 95% confidence intervals (CI) to compare individual nurse and organisational characteristics with high evidence-based practice (EBP) use. Two individual nurse characteristics found to be related to EBP were postgraduate qualifications (OR 1.69, 95% CI 1.07–2.6, p=0.02) and previous research experience (OR 1.9, 95% CI 1.6–2.4, p=0.01). Organisational characteristics related to EBP included access to the internet (OR 2.04, 95% CI 1.3–3.0, p=0.001) and access to ongoing EBP education (OR 1.6, 95% CI 1.1–2.5, p=0.01). Previous research experience (OR 1.6, 95% CI 1.0–2.3, p=0.01) was the only independent predictor of EBP. Given our finding, we suggest that considerably greater effort be made to facilitate nurses involvement in research studies in the perioperative setting.
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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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".