Individual determinants of research utilization: a systematic review
Bibliographic record
Abstract
CONTEXT: In order to design interventions that increase research use in nursing, it is necessary to have an understanding of what influences research use. OBJECTIVE: To report findings on a systematic review of studies that examine individual characteristics of nurses and how they influence the utilization of research. SEARCH STRATEGY: A survey of published articles in English that examine the influence of individual factors on the research utilization behaviour of nurses, without restriction of the study design, from selected computerized databases and hand searches. INCLUSION CRITERIA: Articles had to measure one or more individual determinants of research utilization, measure the dependent variable (research utilization), and evaluate the relationship between the dependent and independent variables. The studies also had to indicate the direction of the relationship between the independent and dependent variables, report a P-value and the statistic used, and indicate the magnitude of the relationship. RESULTS: Six categories of potential individual determinants were identified: beliefs and attitudes, involvement in research activities, information seeking, professional characteristics, education and other socio-economic factors. Research design, sampling, measurement, and statistical analysis were examined to evaluate methodological quality. Methodological problems surfaced in all of the studies and, apart from attitude to research, there was little to suggest that any potential individual determinant influences research use. CONCLUSION: Important conceptual and measurement issues with regard to research utilization could be better addressed if research in the area were undertaken longitudinally by multi-disciplinary teams of researchers.
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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 | 0.047 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".