Factors Influencing Best‐Practice Guideline Implementation: Lessons Learned from Administrators, Nursing Staff, and Project Leaders
Bibliographic record
Abstract
BACKGROUND: Clinical practice guidelines are promising tools for closing the research evidence-practice gap, yet effective and timely implementation of guidelines into practice remains fragmented and inconsistent. Factors influencing effective guideline implementation remain poorly understood, particularly in nursing. A sound understanding of barriers and facilitators is critical for development of effective and targeted guideline implementation strategies. AIM: This paper reports the perceptions of administrators, staff, and project leaders about factors influencing implementation of nursing best practice guidelines. METHODS: Twenty-two organizations, in clusters of two to five, implemented one of seven guidelines in acute, community and long-term care settings. The topics were client centered care, crisis intervention, healthy adolescent development, pain assessment, pressure ulcers, supporting and strengthening families and therapeutic relationships. Fifty-nine administrators, 58 staff and 8 project leaders participated in post implementation semi-structured telephone interviews. Qualitative thematic analysis was conducted. FINDINGS: Factors at individual, organizational and environmental levels were identified as influencing guideline implementation. Facilitators included learning about the guideline through group interaction, positive staff attitudes and beliefs, leadership support, champions, teamwork and collaboration, professional association support, and inter-organizational collaboration and networks. Barriers included negative staff attitudes and beliefs, limited integration of guideline recommendations into organizational structures and processes, time and resource constraints, and organizational and system level change. Similarities and differences in perceptions of these factors were found among staff, project leaders and administrators. IMPLICATIONS/CONCLUSIONS: Best practice guideline implementation strategies should address barriers related to the individual practitioner, social context, and organizational and environmental context, and should be tailored to different groups of stakeholders (i.e., nursing staff, project leaders and administrators). Health care administrators need to recognize the "real" costs and complexity associated with successful implementation of guidelines and the need to ensure corporate commitment at the onset.
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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 | 0.054 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".