Bibliographic record
Abstract
This text is based on the Joanna Briggs Oration, given at the 2005 Joanna Briggs International Conference, Adelaide, Australia. It is printed here with permission. This paper provides an opportunity to reflect on evidence-based nursing. Where have we been? How far we have come? What are the current issues, and where are we going in terms of incorporating high quality evidence into clinical, education, management, and policy decisions? Is evidence-based nursing a passing fad, or does it contribute to quality, efficient health care? Although the use of evidence is often recommended in relation to healthcare reform, institutional change, healthcare practitioner competence, or healthcare practitioner education, opponents argue that there is no evidence that evidence-based healthcare makes a difference. There are no sensitive system indicators; healthcare costs are highly influenced by the adoption and spread of technology; and mortality and morbidity are also influenced by many factors. Yet, evidence-based health care should have an impact on all 3 of these outcomes. One of the earliest reviews to assess the effect of research based nursing practice on patient outcomes identified 84 relevant studies and showed “sizeable gains” in patients’ behavioural, knowledge, physiological, and psychosocial outcomes compared with patients who received routine nursing care.1 However, evidence-based nursing is more than research utilisation. It is the incorporation of the best research evidence along with patient preferences, the clinical setting and circumstances, and healthcare resources into decisions about patient care.2 More recently, Thomas et al updated their review of the use of guidelines by healthcare practitioners other than physicians. They identified 18 studies of 467 healthcare providers (participants were nurses in all but 1 study). Although reporting of methods was poor in all included studies, 3 of 5 studies found improvements in at least some processes of care, and 6 of 8 studies …
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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 | 0.110 | 0.226 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.039 | 0.056 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.026 | 0.051 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".