Bridging Research and Practice through the Nursing Research Facilitator Program in British Columbia
Bibliographic record
Abstract
As Canadian health systems transform to meet changing needs, grounding nursing practice in evidence remains an essential goal for providing safe, high-quality care. nursing research facilitators (NRFs) are strengthening the use of evidence in nursing practice across the province of British Columbia. NRFs are nurses with a research background, whose work is focused on supporting people within health systems to use and do research in their practice and decision-making. Since this role was established in 2009, NRFs have provided facilitative support to over 50 funded research projects, led numerous workshops and journal clubs, and conducted more than 600 research-related consultations. In this paper, we discuss the role and offer exemplars of creative ways in which NRFs are strengthening nurses' engagement in doing and using research by developing capacity for research and evidence-informed practice, building meaningful partnerships and cultivating a culture of curiosity among nurses and other healthcare providers. We reflect on factors contributing to the success of this role and some of the challenges of integration. The paper concludes with a comment on the strategic value of the role.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".