Evidence, Research, Knowledge: A Call for Conceptual Clarity
Bibliographic record
Abstract
OBJECTIVE: To dispel some of the conceptual confusion in the field of evidence-based practice that has resulted from the overlapping use of the terms research, evidence, and knowledge. APPROACH: Theoretical discussion. FINDINGS: Often the terms research and knowledge are used as synonyms for evidence, but the overlap is never complete. The term evidence has long been understood to mean the findings of research. DISCUSSION: Recent attempts to broaden the definition of evidence to include clinical experience and experiential knowledge have been misguided. Broadening our understanding of the basis for clinical decision making and conceptualizing evidence are quite different tasks. Other factors (not other forms of evidence) do shape the clinical decision-making process, but they are not evidence. We might better term them knowledge. Confusing evidence with these other factors has hindered research and the improvement of clinical decision making in health care. We argue that this confusion results from the use of the term evidence when we really mean either research findings or knowledge. CONCLUSIONS: In this article, we have argued for specificity in the use of the term evidence. We urge the restriction of the term evidence to research findings, and while we acknowledge the importance of other influences on the clinical decision-making process, we insist that they are not evidence. The time has come to value personal experience and experiential knowledge for what they are-we should not have to disguise them as types of evidence for them to be deemed of any value. Being specific to language, the goal is to improve clinical decision making by increasing practitioners' reliance on research findings (evidence) while acknowledging (and valuing) the important part played by other forms of knowledge in the decision-making process. The distinctions are important.
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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 | 0.363 | 0.297 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.016 | 0.254 |
| Scholarly communication | 0.048 | 0.108 |
| Open science | 0.015 | 0.034 |
| Research integrity | 0.041 | 0.094 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".