Increasing Understanding of Nursing Research for General Duty Nurses: An Experiential Strategy
Bibliographic record
Abstract
Misconceptions and trepidation about research abound among practicing nurses. However, in light of the movement toward increasing accountability to consumers and the concurrent drive toward evidence-based practice, the need for nursing research can no longer be ignored. Innovative approaches to augment nurses' training and education in research and evidence-based practice must be incorporated into continuing education programs. The Nursing Research and Evidence-Based Practice Committee of a large tertiary care teaching hospital in Winnipeg, Manitoba, Canada, developed a series of opportunities for staff nurses to participate in research projects and have ongoing exposure to the steps in the research process. The Great Canadian Cookie Experiment was an opportunity to participate in quantitative research. Qualitative data from patients' thank you cards were analyzed in an interactive fashion during luncheon seminars held during Nursing Week in 2 subsequent years. A survey of nurses who participated in the luncheon seminars indicated an overall increase in their knowledge about qualitative research methods and an appreciation for participating in the process of nursing research. Continued visibility of nursing research will contribute to changing nurses' attitudes toward fostering an evidence-based approach to clinical practice.
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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 | 0.102 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.013 | 0.030 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.037 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".