Organizational factors that support the implementation of a nursing Best Practice Guideline
Bibliographic record
Abstract
BACKGROUND: The context of the healthcare setting may play a crucial role in influencing the implementation of best practice guidelines in nursing. Further study is required to understand these organizational factors. Two variables, organizational culture and leadership, are thought to influence the adoption of best practice guidelines. AIM: A discussion of organizational factors that influence best practice guideline adoption is presented. A small pilot study is provided as an example of methods for further research. METHODS: A quantitative survey of nursing staff was conducted. RESULTS: Results from the pilot study reveal variability in best practice guideline implementation despite the presence of a culture of organizational learning and transformational leadership. CONCLUSIONS: There is beginning evidence in the literature that culture and leadership are key elements influencing guideline implementation. In this pilot work on two inpatient units where a nursing best practice guideline was implemented, a supportive organizational culture and key people leading change were present. Implications for further studies are offered. IMPLICATIONS FOR NURSING MANAGEMENT: Nursing leaders interested in promoting the use of best practice guidelines must pay attention to the organizational context in which nursing care occurs. A supportive culture where learning is valued coupled with transformational leadership may be key factors in the implementation and the sustainability of best practice guidelines.
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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 | 0.015 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".