Bibliographic record
Abstract
It is through extreme examples or case studies that we can often acutely examine our own values, biases, paradigms, shortcomings or tendencies. In the commentary by Salvage, ‘Politics of Evidence: Conflict and Health in Iraq', there is an unequivocal demonstration that ‘truth is the first casualty of war’. But let's examine this further. Is it only during war that truth gets sacrificed? When do our own values, biases, political persuasions, etc. influence our interpretation and application of evidence? The ‘wars’ in healthcare are usually in the form of budget cuts, re-prioritisation, restructuring, downsizing, amalgamations, regionalisation and established power differentials, etc. It is during these healthcare wars that the value of clinical evidence appears to be most at risk. Who will protect and fight for externally validated evidence during these times? Most recently, there has been a wide propagation of evidence-based practice as a combined process involving systematically produced external evidence, clinical judgement, patient preferences and application context. Although this sounds intuitively and politically appropriate, what are the risks? Is there a risk of downplaying the utility of well-founded external evidence? Will this take us back to square one where we will continue to have wide variations of practices with a wide range of patient and system outcomes? It is clear we do not want evidence-based practice to be akin to a cookbook approach, as this would be disastrous; but, how do we balance between evidence and other aforementioned factors? What guidance can be provided to clinicians, administrators, policy makers, educators and researchers? For the nursing profession, however, it is an unprecedented opportunity to define rigorous processes regarding what to include and what not to include as evidence. The article ‘Comprehensive Systematic Review of Evidence on Developing and Sustaining Nursing Leadership That Fosters a Healthy Work Environment in Healthcare’ demonstrates that we do have room to define the inclusion of both quantitative and qualitative evidence as well as evidence from grey literature within a well-structured and rigorous process of critical appraisal and review of the evidence. And this process can lead to the development of applicable recommendations for practice (albeit in some instances cautious recommendations due to limitations in the availability of the evidence). Development and/or refinement of such processes require continual encouragement and debates such as those proposed by the authors of the article ‘Evidence-Based Decision Making: The Case for Diabetes Care’. Such ongoing debates will allow for a greater range of methodologies to be innovated and tested before the best ones are settled upon. Too early a closure on these debates may be harmful to the contributions that nursing brings in the arena of evidence-based practice. It is the purpose of editorial columns such as this to provoke responses and generally generate healthy discourse. Ask yourself, are we transitioning to Evidence-Informed Practice or are we still in the era of Evidence-Based Practice? Tazim Virani RN MScN Program Director, RNAO Nursing Best Practice Guidelines ProgramCo-Director, Nursing Best Practice Research UnitRNAO, Toronto, Ontario, Canada
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".