A modified evidence-based practice- knowledge, attitudes, behaviour and decisions/outcomes questionnaire is valid across multiple professions involved in pain management
Bibliographic record
Abstract
BACKGROUND: A validated and reliable instrument was developed to knowledge, attitudes and behaviours with respect to evidence-based practice (EBB-KABQ) in medical trainees but requires further adaptation and validation to be applied across different health professionals. METHODS: A modified 33-item evidence-based practice scale (EBP-KABQ) was developed to evaluate EBP perceptions and behaviors in clinicians. An international sample of 673 clinicians interested in treatment of pain (mean age = 45 years, 48% occupational therapists/physical therapists, 25% had more than 5 years of clinical training) completed an online English version of the questionnaire and demographics. Scaling properties (internal consistency, floor/ceiling effects) and construct validity (association with EBP activities, comparator constructs) were examined. A confirmatory factor analysis was used to assess the 4-domain structure EBP knowledge, attitudes, behavior, outcomes/decisions). RESULTS: The EBP-KABQ scale demonstrated high internal consistency (Cronbach's alpha = 0.85), no evident floor/ceiling effects, and support for a priori construct validation hypotheses. A 4-factor structure provided the best fit statistics (CFI =0.89, TLI =0.86, and RMSEA = 0.06). CONCLUSIONS: The EBP-KABQ scale demonstrates promising psychometric properties in this sample. Areas for improvement are described.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".