Evidence and Value: Impact on DEcisionMaking – the EVIDEM framework and potential applications
Bibliographic record
Abstract
BACKGROUND: Healthcare decisionmaking is a complex process relying on disparate types of evidence and value judgments. Our objectives for this study were to develop a practical framework to facilitate decisionmaking in terms of supporting the deliberative process, providing access to evidence, and enhancing the communication of decisions. METHODS: Extensive analyses of the literature and of documented decisionmaking processes around the globe were performed to explore what steps are currently used to make decisions with respect to context (from evidence generation to communication of decision) and thought process (conceptual components of decisions). Needs and methodologies available to support decisionmaking were identified to lay the groundwork for the EVIDEM framework. RESULTS: A framework was developed consisting of seven modules that can evolve over the life cycle of a healthcare intervention. Components of decision that could be quantified, i.e., intrinsic value of a healthcare intervention and quality of evidence available, were organized into matrices. A multicriteria decision analysis (MCDA) Value Matrix (VM) was developed to include the 15 quantifiable components that are currently considered in decisionmaking. A methodology to synthesize the evidence needed for each component of the VM was developed including electronic access to full text source documents. A Quality Matrix was designed to quantify three criteria of quality for the 12 types of evidence usually required by decisionmakers. An integrated system was developed to optimize data analysis, synthesis and validation by experts, compatible with a collaborative structure. CONCLUSION: The EVIDEM framework promotes transparent and efficient healthcare decisionmaking through systematic assessment and dissemination of the evidence and values on which decisions are based. It provides a collaborative framework that could connect all stakeholders and serve the healthcare community at local, national and international levels by allowing sharing of data, resources and values. Validation and further development is needed to explore the full potential of this approach.
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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 | 0.203 | 0.319 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".