An Organizational Intervention to Influence Evidence-Informed Decision Making in Home Health Nursing
Bibliographic record
Abstract
OBJECTIVE: The aims of this study were to field test and evaluate a series of organizational strategies to promote evidence-informed decision making (EIDM) by nurse managers and clinical leaders in home healthcare. BACKGROUND: EIDM is central to delivering high-quality and effective healthcare. Barriers exist and organizational strategies are needed to support EIDM. METHODS: Management and clinical leaders from 4 units participated in a 20-week organization-focused intervention. Preintervention (n = 32) and postintervention (n = 17) surveys and semistructured interviews (n = 15) were completed. RESULTS: Statistically significant increases were found on 4 of 31 survey items reflecting an increased organizational capacity for participants to acquire and apply research evidence in decision making. Support from designated facilitators with advanced skills in finding, appraising, and applying research was the highest rated intervention strategy. CONCLUSIONS: Results are useful to inform the development of organizational infrastructures to increase EIDM capacity in community-based healthcare organizations.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".