Influence of Organizational Characteristics and Context on Research Utilization
Bibliographic record
Abstract
BACKGROUND: Despite three decades of empirical investigation into research utilization and a renewed emphasis on evidence-based medicine and evidence-based practice in the past decade, understanding of factors influencing research uptake in nursing remains limited. There is, however, increased awareness that organizational influences are important. OBJECTIVES: To develop and test a theoretical model of organizational influences that predict research utilization by nurses and to assess the influence of varying degrees of context, based on the Promoting Action on Research Implementation in Health Services (PARIHS) framework, on research utilization and other variables. METHODS: The study sample was drawn from a census of registered nurses working in acute care hospitals in Alberta, Canada, accessed through their professional licensing body (n = 6,526 nurses; 52.8% response rate). Three variables that measured PARIHS dimensions of context (culture, leadership, and evaluation) were used to sort cases into one of four mutually exclusive data sets that reflected less positive to more positive context. Then, a theoretical model of hospital- and unit-level influences on research utilization was developed and tested, using structural equation modeling, and 300 cases were randomly selected from each of the four data sets. RESULTS: Model test results were as follows--low context: chi2= 124.5, df = 80, p <. 001; partially low: chi2= 144.2, p <. 001, df = 80; partially high: chi2= 157.3, df = 80, p <. 001; and partially low: chi2= 146.0, df = 80, p <. 001. Hospital characteristics that positively influenced research utilization by nurses were staff development, opportunity for nurse-to-nurse collaboration, and staffing and support services. Increased emotional exhaustion led to less reported research utilization and higher rates of patient and nurse adverse events. Nurses working in contexts with more positive culture, leadership, and evaluation also reported significantly more research utilization, staff development, and lower rates of patient and staff adverse events than did nurses working in less positive contexts (i.e., those that lacked positive culture, leadership, or evaluation). CONCLUSION: The findings highlight the combined importance of culture, leadership, and evaluation to increase research utilization and improve patient safety. The findings may serve to strengthen the PARIHS framework and to suggest that, although it is not fully developed, the framework is an appropriate guide to implement research into 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 | 0.016 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".