Framework of policy recommendations for implementation of evidence-based practice: a systematic scoping review
Bibliographic record
Abstract
OBJECTIVES: Evidence-based practice (EBP) may help improve healthcare quality. However, not all healthcare professionals and managers use EBP in their daily practice. We systematically reviewed the literature to summarise self-reported appreciation of EBP and organisational infrastructure solutions proposed to promote EBP. DESIGN: Systematic review. Two investigators independently performed the systematic reviewing process. INFORMATION SOURCES: MEDLINE, EMBASE and Cochrane Library were searched for publications between 2000 and 2011. ELIGIBILITY CRITERIA FOR INCLUDED STUDIES: Reviews and surveys of EBP attitude, knowledge, awareness, skills, barriers and facilitators among managers, doctors and nurses in clinical settings. RESULTS: We found 31 surveys of fairly good quality. General attitude towards EBP was welcoming. Respondents perceived several barriers, but also many facilitators for EBP implementation. Solutions were proposed at various organisational levels, including (inter)national associations and hospital management promoting EBP, pregraduate and postgraduate education, as well as individual support by EBP mentors on the wards to move EBP from the classroom to the bedside. CONCLUSIONS: More than 20 years after its introduction, the EBP paradigm has been embraced by healthcare professionals as an important means to improve quality of patient care, but its implementation is still deficient. Policy exerted at microlevel , middlelevel and macrolevel, and supported by professional, educational and managerial role models, may further facilitate EBP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.490 | 0.554 |
| Meta-epidemiology (narrow) | 0.009 | 0.010 |
| Meta-epidemiology (broad) | 0.024 | 0.026 |
| Bibliometrics | 0.064 | 0.040 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.030 | 0.033 |
| Open science | 0.022 | 0.026 |
| Research integrity | 0.043 | 0.024 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".