Research capacity in nursing: a concept analysis based on a scoping review
Bibliographic record
Abstract
OBJECTIVE: As the discipline of nursing has advanced, research capacity in nursing has become increasingly important to the discipline's development. However, research capacity in nursing is still commonly used as a buzzword, without a consistent and clear definition. The purpose of this study is to clarify the concept of research capacity in nursing by identifying its conceptual components in the relevant nursing literature using the Pragmatic Utility method. DESIGN: A Pragmatic Utility concept analysis based on a scoping review. DATA SOURCES: Academic literature retrieved from PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, Scopus, Web of Science and ProQuest Dissertations and Theses (PQDT). ELIGIBILITY CRITERIA: Qualitative studies, quantitative studies, mixed method studies or literature reviews focusing on research capacity in nursing published in English between 2009 and 2019. RESULTS: Competence, motivation, infrastructure and collaboration for nursing research are the antecedents of research capacity in nursing. The attributes of research capacity in nursing are 'non-individual level', 'context-embeddedness' and 'sustainability'. The direct outcome of research capacity in nursing is nursing research. The allied concepts identified are nursing research competency, nursing research capability and evidence-based practice capacity in nursing. CONCLUSIONS: Research capacity in nursing is the ability to conduct nursing research activities in a sustainable manner in a specific context, and it is normally used at a non-individual level. Research capacity in nursing is critical for the development of the nursing discipline, and for positive nurse, patient and healthcare system outcomes. More studies are needed to further explore the allied concepts of research capacity in nursing, and to better understand relationships among these allied concepts.
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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 | 0.107 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.062 | 0.049 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".