Determinants of Implementation of Evidence-Based Practice in Clinical Decision-Making among Nurses in Primary Health Care Facilities
Bibliographic record
Abstract
Background. Healthcare workers and institutions often face a lack of evidence-based and cost-effective care, which, if addressed, could improve practice and outcomes. Aim. To assess the determinants of implementation of evidence-based practice (EBP) in clinical decision-making (CDM) among nurses in primary health care facilities. Methods. The study utilized a descriptive cross-sectional design involving 266 nurses from primary healthcare facilities in Ondo State. Data were collected through a structured questionnaire administered via the online survey platform Google Forms. Findings are presented in frequency and percentage tables, and inferential statistics were analyzed using Chi-square tests at a 5% level of statistical significance. Results. Knowledge has been identified as a key determinant in the implementation of Evidence-Based Practice (EBP) in Chronic Disease Management (CDM) among nurses in primary healthcare facilities. The study revealed that 73.7% of respondents demonstrated a strong knowledge of EBP in CDM, with the highest levels reported among ward managers. Additionally, access to EBP resources and consistent support from employers were major factors influencing successful EBP implementation, with 86% of respondents affirming the importance of these supports. Regarding implementation levels, 53.4% of respondents reported high implementation of EBP in CDM, while the remaining 46.6% indicated low implementation. Conclusions. The study concluded that while nurses generally possessed good knowledge of Evidence-Based Practice, the highest levels of expertise were observed among ward managers. Key barriers to Evidence-Based Practice implementation in Chronic Disease Management were also identified, including limited network accessibility and inconsistent electricity supply. Keywords: determinants; implementation; evidence-based practice; clinical decision-making; nurses.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".