The relationship between busyness and research utilization: it is about time
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To explore the concept of busyness in nursing and to understand the relationship between busyness and nurses' research utilization better. BACKGROUND: Lack of time and busyness are consistently reported as barriers to research utilization. Current literature fails to identify the dimensions of busyness and offers little insight into the relationship between busyness and nurses' research utilization. DESIGN/METHODS: We performed a secondary analysis of qualitative data and created a conceptual map of busyness in nursing. RESULTS: Our results suggested that busyness consists of physical and psychological dimensions. Interpersonal and environmental factors influenced both dimensions. Cultural and intrapersonal factors contributed to psychological elements. The effects of busyness reported included missed opportunities, compromised safety, emotional and physical strain, sacrifice of personal time, incomplete nursing care and the inability to find or use resources. CONCLUSIONS: Our beginning description of busyness contributes to a greater understanding of the relationship between busyness and research utilization. Our findings suggest that lack of time as a barrier to research utilization is more complex than depicted in the literature. Instead, the mental time and energy required to navigate complex environments and a culture of busyness more accurately reflect what may be meant by 'lack of time' as a barrier to research utilization. RELEVANCE TO CLINICAL PRACTICE: Future interventions aimed at increasing research utilization may be more effective if they focus on factors that contribute to a culture of busyness in nursing and address the mental time and energy required for nurses to use research 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 | 0.026 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".