Evidence-Based Practice in Healthcare: An Exploratory Cross-Discipline Comparison of Enhancers and Barriers
Bibliographic record
Abstract
In order to improve health outcomes, healthcare providers need to base practice on current evidence. The purpose of this qualitative study was to explore and compare the understanding and experiences with evidence-based practice (EBP) in three different disciplines. Researchers conducted individual interviews with psychiatrists, nurses, and dental hygienists. The majority of study participants demonstrated an understanding of EBP and were able to identify enhancers and barriers to implementing EBP. Using a grounded theory approach, several major themes acting as enhancers and barriers to EBP emerged and revealed both differences and similarities within and across the three health disciplines. While saturation was not attempted, this exploratory research is important in contributing to understanding the cultural practice milieu in relation to individual characteristics in implementing evidence into practice with the overall aim of improving healthcare delivery and outcomes.
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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 | 0.054 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".