Creation and validation of the evidence‐based practice confidence scale for health care professionals
Bibliographic record
Abstract
RATIONALE: Self-efficacy beliefs may provide a means to influence health care professionals' (HCPs) engagement in evidence-based practice (EBP) but no standardized measure of this construct exists. OBJECTIVES: To create and evaluate the validity and comprehensibility of a scale measuring belief in ability to implement EBP, known as EBP self-efficacy, among HCPs. METHODS: Items describing the steps of EBP outlined in the literature were generated. Fourteen content experts reviewed the scale for face and content validity. A purposive sample of 10 HCPs from medicine, nursing, physical and occupational therapy and speech language pathology provided feedback on the clarity and meaning of scale wording in telephone interviews. RESULTS: Progressive refinement yielded an 11-item self-report scale. Each item describes an activity that is part of the process of implementing EBP, such as formulating a question to guide a literature search and asking your patient or client about his/her needs, values and treatment preferences. To complete the scale, HCPs rate their level of confidence on an 11-point scale ranging from 0% (no confidence) to 100% (completely confident) in their ability to perform each activity. Item-level responses are averaged to obtain a summary score that can range from 0% to 100%. CONCLUSION: The newly created scale, named the EPIC (evidence-based practice confidence) scale, provides an opportunity to evaluate HCPs' beliefs in their ability to implement EBP and the effects of interventions on these beliefs. Psychometric evaluation of the test-retest reliability and construct validity of the scale is necessary prior to its widespread use.
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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 | 0.040 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".