National Study of Nursing Research Characteristics at Magnet®-Designated Hospitals
Bibliographic record
Abstract
OBJECTIVE: To describe the research infrastructure, culture, and characteristics of building a nursing research program in Magnet®-designated hospitals. BACKGROUND: Magnet recognition requires hospitals to conduct research and implement evidence-based practice (EBP). Yet, the essential characteristics of productive nursing research programs are not well described. METHODS: We surveyed 181 nursing research leaders at Magnet-designated hospitals to assess the characteristics in their hospitals associated with research infrastructure, research culture, and building a nursing research program. RESULTS: Magnet hospitals provide most of the needed research infrastructure and have a culture that support nursing research. Higher scores for the 3 categories were found when hospitals had a nursing research director, a research department, and more than 10 nurse-led research studies in the past 5 years. CONCLUSIONS: While some respondents indicated their nurse executives and leaders support the enculturation of EBP and research, there continue to be barriers to full implementation of these characteristics in 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".