Building Capacity for Evidence‐Based Practice: Understanding How Licensed Practical Nurses (LPNs) Source Knowledge
Bibliographic record
Abstract
BACKGROUND: In Canada, all nurses are required to engage in evidence-based practice (EBP) as an entry-to-practice competency; however, there is little research that examines Licensed Practical Nurses' (LPNs') information seeking behaviors or preferred sources of knowledge to conduct EBP. AIMS: Due to the differences in education and roles of LPNs and Registered Nurses (RNs), it is both necessary and important to gain an understanding of how LPNs utilize evidence in their unique nursing practice. The purpose of this study was to investigate how LPNs source knowledge for their nursing practice. METHODS: A descriptive, cross-sectional survey of LPNs from Alberta, Canada asked participants to rank sources of knowledge that inform their practice. Responses were correlated with age and years of practice. Analysis of variance was used to determine if there were significant mean differences between average scores and place of employment. RESULTS: LPN participants used similar sources of knowledge as RNs. The top source of knowledge for both RNs and LPNs was the information they learn about each individual client and the least utilized sources of knowledge were articles published in nursing, medical, and research journals, tradition, and popular media. This finding is consistent with previous studies on RNs that found nurses do not often access current research evidence to inform their practice. LINKING EVIDENCE TO ACTION: Since relatively few LPNs access nursing and research journals, it is important to tailor EBP education information to the workplace context. Future avenues of research might explore the potential of using in-services and webinars to disseminate information and skills training on EBP to the LPNs, as this was a popular source of practice 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 | 0.031 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".