A structured process to develop scenarios for use in evaluation of an evidence-based approach in clinical decision making
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Scenarios are used as the basis from which to evaluate the use of the components of evidence-based practice in decision making, yet there are few examples of a standardized process of scenario writing. The aim of this paper is to describe a step-by-step scenario writing method used in the context of the authors' curriculum research study. METHODS: Scenario writing teams included one physical therapy clinician and one academic staff member. There were four steps in the scenario development process: (1) identify prevalent condition and brainstorm interventions; (2) literature search; (3) develop scenario framework; and (4) write scenario. RESULTS: Scenarios focused only on interventions, not diagnostic or prognostic problems. The process led to two types of scenarios - ones that provided an intervention with strong research evidence and others where the intervention had weak evidence to support its use. The end product of the process was a scenario that incorporates aspects of evidence-based decision making and can be used as the basis for evaluation. CONCLUSION: The use of scenarios has been very helpful to capture therapists' reasoning processes. The scenario development process was applied in an education context as part of a final evaluation of graduating clinical physical therapy students.
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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 | 0.040 | 0.319 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 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; both teacher heads 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".