Insights about the process and impact of implementing nursing guidelines on delivery of care in hospitals and community settings
Bibliographic record
Abstract
BACKGROUND: Little is known about the impact of implementing nursing-oriented best practice guidelines on the delivery of patient care in either hospital or community settings. METHODS: A naturalistic study with a prospective, before and after design documented the implementation of six newly developed nursing best practice guidelines (asthma, breastfeeding, delirium-dementia-depression (DDD), foot complications in diabetes, smoking cessation and venous leg ulcers). Eleven health care organisations were selected for a one-year project. At each site, clinical resource nurses (CRNs) worked with managers and a multidisciplinary steering committee to conduct an environmental scan and develop an action plan of activities (i.e. education sessions, policy review). Process and patient outcomes were assessed by chart audit (n = 681 pre-implementation, 592 post-implementation). Outcomes were also assessed for four of six topics by in-hospital/home interviews (n = 261 pre-implementation, 232 post-implementation) and follow-up telephone interviews (n = 152 pre, 121 post). Interviews were conducted with 83/95 (87%) CRN's, nurses and administrators to describe recommendations selected, strategies used and participants' perceived facilitators and barriers to guideline implementation. RESULTS: While statistically significant improvements in 5% to 83% of indicators were observed in each organization, more than 80% of indicators for breastfeeding, DDD and smoking cessation did not change. Statistically significant improvements were found in > 50% of indicators for asthma (52%), diabetes foot care (83%) and venous leg ulcers (60%). Organizations with > 50% improvements reported two unique implementation strategies which included hands-on skill practice sessions for nurses and the development of new patient education materials. Key facilitators for all organizations included education sessions as well as support from champions and managers while key barriers were lack of time, workload pressure and staff resistance. CONCLUSION: Implementation of nursing best practice guidelines can result in improved practice and patient outcomes across diverse settings yet many indicators remained unchanged. Mobilization of the nursing workforce to actively implement guidelines and to monitor the delivery of their care is important so that patients may learn about and receive recommended healthcare.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".