The Nurse as Bricoleur in Falls Prevention: Learning from a Case Study of the Implementation of Fall Prevention Best Practices
Bibliographic record
Abstract
BACKGROUND: Falls prevention in "real-life" clinical practice is a complex undertaking. Nurses play an active and essential role in falls prevention. AIM: This discussion paper presents a picture of the nurse as a bricoleur in falls prevention, requiring knowledge in many areas and the ability to perform multiple diverse tasks. METHODS: Building on a qualitative case study with nurses at various levels in three acute care facilities, this paper posits that the concept of nurse as bricoleur has the potential to broaden our understanding of the complexity of falls prevention. FINDINGS: The nurse as bricoleur within the Promoting Action Research in Health Services framework as the provider of person- or patient-centered evidence-based care is conceptualized. Within this framework, the nurse uses his or her professional knowledge or clinical experience while considering research, local data, and information, and the patient's experience and preferences to provide this care, the bricolage. Each of these areas is discussed as well as the impact on the nurse when a fall does occur. LINKING EVIDENCE TO ACTION: Recognizing this complexity of the nurses' world has important implications for both service delivery and education, including preparation of students, and the implementation of new organizational initiatives and supports for nurses when falls do occur despite the best efforts of all involved.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".