Investing in Nursing Research in Practice Settings: A Blueprint for Building Capacity
Bibliographic record
Abstract
Engaging clinical nurses in practice-based research is a cornerstone of professional nursing practice and a critical element in the delivery of high-quality patient care. Practising staff nurses are well suited to identify the phenomena and issues that are clinically relevant and appropriate for research. In response to the need to invest in and build capacity in nursing research, hospitals have developed creative approaches to spark interest in nursing research and to equip clinical nurses with research competencies. This paper outlines a Canadian hospital's efforts to build research capacity as a key strategy to foster efficacious, safe and cost-effective patient care practices. Within a multi-pronged framework, several strategies are described that collectively resulted in enhanced research and knowledge translation productivity aimed at improving the delivery of safe and high-quality patient care.
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 | 0.349 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.018 | 0.130 |
| Scholarly communication | 0.050 | 0.037 |
| Open science | 0.008 | 0.060 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".