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 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 | 0.130 | 0.243 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".