Bibliographic record
Abstract
BACKGROUND: Research utilization is the use of research to guide clinical practice. However, little is known about the characteristics of the research utilization literature in nursing, including the development and organization of this field of study. This article addresses the knowledge gap in this field of study by bibliometrically analyzing the research utilization literature in nursing. OBJECTIVE: To map research utilization as a field of study in nursing using bibliometric methods, and to identify the structure of this scientific community, including the current network of researchers. METHOD: A search of electronic and hard copy databases resulted in bibliographic data for 630 articles on research utilization in nursing published between 1972 and 2001. Bibliometric techniques used included a statistical analysis of publication counts, co-word analysis, and co-citation analysis. RESULTS: The analyses showed a trend of increased productivity since the early 1990s. Most publications were authored by a single author, with no tendency toward increased collaboration over time. Most references cited in the articles were nursing references, indicating that there is very little flow into nursing from other fields. Only 4% of the references cited were actual research articles about research utilization, consistent with applied fields in which clinicians most commonly cite other clinicians. The 630 articles were published in a total of 194 different journals, with the Journal of Advanced Nursing identified as a key journal in the field. CONCLUSIONS: According to the analysis, tremendous growth has occurred in the field of research utilization. However, the limited amount of collaborative research and the repeated citation of a few references indicate that the field is under-developed. The research utilization field would benefit from more substantive conceptual and empirical work, and more collaboration among emerging scholars.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 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; 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".