Does self-efficacy influence the application of evidence-based practice: A survey and structural equation model
Bibliographic record
Abstract
Background: Implementation of Evidence-Based Practice (EBP) is complex and consequently, even within organizations that have made efforts to promote EBP use, EBP is often underutilized by individual clinicians. Purpose: The aim of our study was to better understand the relationship between self-efficacy and EBP implementation in clinical environments that have undergone efforts to increase EBP utilization. We suggest that EBP is a set of behaviors that result from individuals acquiring, applying, and sharing new knowledge with others in the organization. We hypothesize, based upon a social cognitive theoretical approach, that these behaviors are influenced by clinician perception of self-efficacy. Methods: We analyzed data provided by a 2011 survey of clinicians working within a national sample of hospitals that were actively participating in the Clinical Practice Model Resource Center (CPMRC), a collaborative consortium of health care organizations working to guide the implementation of EBP into the work worlds of clinicians. Structural equation modeling was used to examine the relationships between the acquisition, application, and sharing of evidence, and self-efficacy. Results: Self-efficacy increased the acquisition of evidence and application of evidence. Self-efficacy did not exert a significant influence on sharing evidence. Acquisition of evidence increased both application of evidence and sharing of evidence. Application of evidence then increased sharing of evidence among sampled clinicians. Conclusions: Efforts to promote self-efficacy in clinicians are powerful in that they directly promote the acquisition and application of evidence, and indirectly encourage communication between clinicians in an effort to justify and reinforce new knowledge. Efforts to boost self-efficacy would benefit from a focus on helping clinicians become more comfortable with actions related to acquiring evidence and generating new knowledge.
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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 | 0.027 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".