Landmark Articles From Volumes 31-40 / Des articles-jalons tirés des volumes 31 à 40 - Mapping the Research Utilization Field in Nursing
Bibliographic record
Abstract
The recent increase in interest in the field of research utilization, often embedded in the notions of evidence-based practice, presents a rich opportunity to advance the field in nursing. While an extensive literature on the subject exists in nursing, close examination reveals that much of it is opinion and anecdotal literature, and that sustained and programmatic theory building and testing in this field has been sporadic at best. This article maps the field of research utilization, proposing that we focus on major areas of inquiry: scientific, historical, and philosophical foundations, synthesis, determinants, policy, interventions to increase research utilization, and outcomes. In so doing, alternative ways of viewing and conceptualizing this field are possible. In conducting the kinds of studies and supporting the kinds of programs identified in this map, nursing, in collaboration with appropriate partners, can significantly advance the field of research dissemination and utilization studies and practice at many levels in the health system.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.119 | 0.040 |
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".