Identifying candidate items for a prototype index on propensity to integrate research evidence into clinical decision-making in rehabilitation
Bibliographic record
Abstract
Abstract Purpose Existing measures of evidence-based practice (EBP) in rehabilitation provide a fragmented interpretation of EBP competencies as performance is rated on discrete domains without a harmonized measure to represent the multidimensionality of EBP. Building on previous work, this study aimed to provide evidence that a brief multidimensional index can be formed to determine a clinician’s propensity to integrate research evidence into decision-making to inform the subsequent development process. Methods Using a Canadian dataset of occupational and physical therapists (n=127) who responded to a survey containing 70 frequently used items to measure EBP (representing six domains), one item per key EBP domain was selected using Rasch measurement theory and expert consensus. A preliminary scoring algorithm was developed for testing purposes. The interpretability of the prototype index was examined across characteristics of the sample and compared to full EBP measures using generalized estimating equations. Results Five items were selected for inclusion in the prototype index representing the dimensions of use of research evidence, self-efficacy, resources, attitudes, and activities . Testing demonstrated that the prototype behaves consistently regardless of age groups, gender, and setting, and provides comparable information to full EBP measures. Conclusion This study provides promising preliminary evidence to justify continuing the index development process. The benefits of having a global index of EBP as opposed to having multiple domain-specific measures are discussed in this paper.
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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 | 0.037 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".