Evaluating the Psychometric Properties of the Evidence-Based Practice Attitude and Utilization Survey
Bibliographic record
Abstract
OBJECTIVE: Most health professions recognize the value of evidence-based practice (EBP), yet the uptake of EBP across most health disciplines has been suboptimal. To improve EBP uptake, it is important to first understand the many dimensions that affect EBP use. The Evidence-Based practice Attitude and utilization SurvEy (EBASE) was designed to measure the attitudes, skills, and use of EBP among practitioners of complementary and alternative medicine (CAM); however, the dimensionality of the instrument is not well understood. The aim of the current research was to examine the psychometric properties of the attitudes, skills, and use subscales of EBASE. DESIGN: This was a secondary analysis of data obtained from the administration of EBASE. Data were examined using principal components analyses and confirmatory methods. Internal consistency reliabilities of resultant subscales were also computed. PARTICIPANTS: 1314 U.S. chiropractors and 554 Canadian chiropractors. RESULTS: A unidimensional structure best fit the attitudes and use subscales. Skills subscale items were best represented by subscales with a multidimensional structure. Specifically, the skills construct was best modeled with three dimensions (identification of the research question, locating research, and application of EBP). All subscales had acceptable internal consistency reliability estimates. CONCLUSIONS: The findings support the modification of the scoring guidelines for the original EBASE. These changes are likely to result in a more accurate measure of EBP attitudes, skills, and use among chiropractors, and possibly CAM providers more generally.
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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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".