Supporting the Uptake of Nursing Guidelines: What You Really Need to Know to Move Nursing Guidelines into Practice
Bibliographic record
Abstract
BACKGROUND: There is a current push to use best practice guidelines (BPGs) in health care to enhance client care and outcomes. Even though intensive resources have been invested internationally to develop BPGs, a gap in knowledge exists about how to consistently and efficiently move them into practice. METHODS: Constructivist grounded theory was used to explore the complex processes of a breastfeeding BPG implementation and uptake in three acute care hospitals. Interviews (n = 120) with 112 participants representing clients, nurses, lactation consultants, midwives, physicians, managers, administrators, and nurse educators as well as document and field note analysis informed this study. Data were analyzed using constant comparison and coding steps outlined by Charmaz: initial coding, selective (focused) coding, then theoretical coding. Triangulation of data types and sources were used as well as theoretical sampling. Data were collected from 2009 to 2010. RESULTS: Two sites showed BPG uptake while one did not. Factors present in the uptake sites included, ongoing passionate frontline leaders, the use of multifaceted strategies, and processes that occurred at organizational, leadership, individual and social levels. Particularly noteworthy was the transformation of individual nurses to believing in and using the BPG. Impacts occurred at client, nurse, unit, inter-professional, organizational and system levels. CONCLUSIONS: A conceptual framework: Supporting the Uptake of Nursing Guidelines, was developed that reveals essential processes used to facilitate BPG uptake into nursing practice and a process of nurse transformation to believing in and using the BPG.
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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 | 0.057 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.013 | 0.030 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".