Building the Research Enterprise in the Academic Environment
Bibliographic record
Abstract
The preponderance of nursing research conducted in the United States occurs in schools of nursing. Accordingly, a major role for academic leaders in nursing education is the development of a resource base to support and expand the research mission of the nursing program. The intersection of research and practice is also an essential element for assuring the relevance of nursing research and advancing the application of the evidence generated by nursing scientists. The following paper presents an introduction to nursing research in the U.S. with an emphasis on the educational and operational resources needed to maintain a robust research enterprise in schools of nursing. Key supports for this important work are profiled, including federal agencies and programs committed to advancing nursing science and the more widespread engagement of nurses in team-based research. The paper concludes with a look at efforts underway to enhance quality in research-focused doctoral programs and an assessment of critical roles nursing deans and faculty play in championing nursing research and preparing the next generation of nurse scientists.
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 | 0.367 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.029 | 0.086 |
| Scholarly communication | 0.090 | 0.055 |
| Open science | 0.006 | 0.063 |
| Research integrity | 0.016 | 0.030 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".