Sigma Theta Tau International Position Statement on Evidence‐Based Practice February 2007 Summary
Bibliographic record
Abstract
Authored on behalf of the Sigma Theta Tau International 2005-2007 Research and Scholarship Advisory Committee: Laura Cullen, RN, MA, APN, Evidence-Based Practice Coordinator, Research, Quality and Outcomes Management, Department of Nursing Services and Patient Care, University of Iowa Hospitals and Clinics, Iowa City, Iowa. Alba DiCenso, RN, PhD, CHSRF/CIHR Chair in Advanced Practice Nursing, Director of the CHSRF/CIHR Ontario Training Centre in Health Services and Policy Research, McMaster University School of Nursing, Hamilton, Ontario, Canada. Rhonda Griffiths, RN, Cm, BEd Nursing, MSc(Hons), PhD, Professor of Nursing, University of Western Sydney, Director of the South West Sydney Centre for Applied Nursing Research (CANR), Director of the NSW Centre for Evidence Based Health Care (The Joanna Briggs Institute), Sydney, New South Wales, Australia. Brendan McCormack, RGN, DPhil(Oxon.), BSc(Hons.), PGCEA, RMN, DPN Coordinator, Director of Nursing Research and Practice Development, Royal Group of Hospitals, Belfast and University of Ulster at Jordanstown, Adjunct Professor, Faculty of Medicine, Nursing & Health Sciences, Monash University, Melbourne, Australia Belfast, Northern Ireland. Jo Rycroft-Malone, RN, PhD, MSc, BSc(Hons), Wales Reader in Health Services Research; Centre for Health-Related Research; School for Healthcare Sciences; College of Health & Behavioural Sciences; University of Wales, Bangor, Wales.
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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 | 0.072 | 0.167 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.024 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.092 | 0.109 |
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".