Development and Validation of a Derived Measure of Research Utilization by Nurses
Bibliographic record
Abstract
BACKGROUND: Theoretical models are needed to guide strategies for the implementation of research into clinical practice. To develop and test such models, including analyses of complex theoretical constructs and causal relationships, rich datasets are needed. Working with existing datasets may mean that important variables are lacking. OBJECTIVE: The aim of this study was to derive a nursing research utilization variable and validate it using the Promoting Action on Research Implementation in Health Services (PARIHS) conceptual framework on research implementation. METHODS: This study was based on data from two surveys of registered nurses. The first survey (1996; N = 600) contained robust research utilization variables but few organizational variables. The second (1998; N = 6,526) was rich in organizational variables but contained no research utilization variables. A linear regression model with predictors common to both datasets was used to derive a research utilization variable in the 1998 dataset. To validate these scores, four separate procedures based on the hypothesis of a positive relationship between context and research utilization were completed. Mutually exclusive groups reflecting various levels of context were created to accomplish these procedures. RESULTS: The derived research utilization variable was successfully mapped onto the cases in the 1998 dataset. The derived scores ranged from 0.21 to 21.40, with a mean of 10.85 (SD = 3.23). The mean score per subgroup ranged from 8.28 for the lowest context group to 12.75 for the highest context group. One of the validation procedures showed that significant differences in mean research utilization existed only among four conceptually unique context groups (p < .001). These groups showed a positive incremental relationship in research utilization (p < .001; the better the context, the higher the research utilization score). The validity of the derived variable was supported by using the three remaining validation procedures. DISCUSSION: The successful creation and validation of a derived research utilization variable will enable advanced modeling of the relationships between research utilization and individual and organizational characteristics. The findings also support the construct validity of the context element of the PARIHS theoretical framework.
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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 | 0.051 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".