Evidence and Value: Impact on DEcisionMaking – the EVIDEM framework and potential applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it