Creation of a Tool for Assessing Knowledge in Evidence-Based Decision-Making in Practicing Health Care Providers
Bibliographic record
Abstract
INTRODUCTION: Evidence-based decision-making (EBDM) is the process health care providers (HCPs) use to identify and appraise potential evidence. It supports the integration of best research evidence with clinical expertise and patient values into the decision-making process for patient care. Competence in this process is essential to delivery of optimal care. There is no objective tool that assesses EBDM across HCP groups. This research aimed to develop a content valid tool to assess knowledge of the principles of evidence-based medicine and the EBDM process, for use with all HCPs. METHODS: A Delphi process was used in the creation of the tool. Pilot testing established its content validity with the added benefit of evaluating HCPs' knowledge of EBDM. Descriptive statistics and multivariate mixed models were used to evaluate individual survey responses in total, as well as within each EBDM component. RESULTS: The tool consisted of 26 multiple-choice questions. A total of 12,884 HCPs in Nova Scotia were invited to participate in the web-based validation study, yielding 818 (6.3%) participants, 471 of whom completed all questions. The mean overall score was 68%. Knowledge in one component, integration of evidence with clinical expertise and patient preferences, was identified as needing development across all HCPs surveyed. DISCUSSION: A content valid tool for assessing HCP EBDM knowledge was created and can be used to support the development of continuing education programs to enhance EBDM competency.
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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 | 0.064 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".