Development of five online modules for teaching evidence-informed healthcare: the West coast Interprofessional Clinical Knowledge Evidence Disseminator (WICKED) Project
Bibliographic record
Abstract
Evidence-informed healthcare (EIHC) is a systematic approach to clinical problem solving that facilitates the integration of the best available research evidence with clinical expertise and our patient’s unique values and circumstances.1 To become an EIHC practitioner requires knowledge, skills and practice. The five-step model of EIHC (asking answerable clinical questions, acquiring the evidence, appraising the evidence, applying the evidence and assessing performance as an EIHC practitioner) forms the basis for both teaching EIHC and clinical practice.2 Despite many EIHC success stories, variation in the adoption of evidence-based practice remains a problem.3 Barriers to implementing EIHC are well documented, with lack of resources being the most common barrier,4 followed by lack of knowledge and skills about appraisal, negative perceptions about research, lack of resources and time, low self-efficacy, inadequate access to the literature and financial barriers.4 5 Some of these barriers are directly related to the steps of EIHC, indicating that there is a clear need to improve the teaching of EIHC across all professions. Interventions using multiple methods are most likely to improve knowledge and skills compared with single interventions or no interventions,6 with the most effective teaching strategies being those that are interactive and clinically integrated.7 Online learning with high levels of interactivity is increasingly used as a learning intervention8 and is as effective a strategy as lecture-based teaching.9 Online learning has the added advantage of providing learner-centred access to course materials at a time and place convenient to them and to tailor their learning to their own timing, pace and needs.8 Virtual patient cases are designed to represent real-life clinical scenarios and are well suited for facilitating the development of clinical reasoning skills,10 an essential element of EIHC. Cases designed using a problem-oriented training approach increase the …
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".