Reconsidering healthcare evidence as dynamic and distributed
Bibliographic record
Abstract
AIM: The basic thrust of evidence-based healthcare is that current best evidence should be used explicitly and judiciously for diagnosis, management, and other activities in healthcare settings. For this to be possible, researchers, practitioners, and other stakeholders must have a clear and accurate conceptualization of what constitutes 'evidence' in healthcare environments, and the manner in which it is used in decision-making and other activities. Currently, the dominant conceptualization of evidence is that of a body of information that can be retrieved by stakeholders for use in healthcare practice. The aim of this article is to critically examine the concept of evidence, particularly in light of recent models of human cognition and information use in decision-making and other cognitive activities. METHODS: In this theoretical article, we employ both analytical and synthetic methods to critically examine the concepts under investigation. Key concepts, such as evidence and information, and the essential relationships between them are analyzed from the vantage point of cognitive science, information science, and other relevant disciplines to explicate a conceptualization of evidence that moves past static and objectivist accounts. RESULTS: We demonstrate that evidence is fundamentally information that takes various forms-i.e., artifacts, mental structures, or communication processes. Specific forms and manifestations of evidence can thus be described in the context of information use in dynamic information environments. Furthermore, evidence-based healthcare activities are shown to be fundamentally cognitive in nature. For any given evidence-based healthcare activity, its quality and outcome can be understood in the context of how different sources of evidence are coordinated within a distributed cognitive system. In this sense, evidence based health care activity becomes more a matter of understanding the movement of information and knowledge within a distributed and dynamic cognitive system than mere access to or translation of a ready-at-hand resource. CONCLUSIONS: The conceptualization of evidence presented in this article has a number of implications for evidence-based healthcare-in terms of where attention is focused, the direction of future research efforts, how evidence generation, use, and practice are conceptualized and discussed, and how healthcare technologies are designed and evaluated. Furthermore, the conceptualization presented in this article has implications for the manner in which evidence 'hierarchies' are developed. Such hierarchies do not provide a complete picture of evidence and the way it is used in healthcare activities. Understanding the dynamic nature of evidence and its role in distributed cognitive activities may lead to more robust and multi-faceted taxonomies, frameworks, and hierarchies related to evidence-based healthcare.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".