The Relationship Among Evidence‐Based Practice and Client Dyspnea, Pain, Falls, and Pressure Ulcer Outcomes in the Community Setting
Bibliographic record
Abstract
BACKGROUND: There are gaps in knowledge about the extent to which home care nurses' practice is based on best evidence and whether evidence-based practice impacts patient outcomes. AIM: The purpose of this study was to investigate the relationship between evidence-based practice and client pain, dyspnea, falls, and pressure ulcer outcomes in the home care setting. Evidence-based practice was defined as nursing interventions based on best practice guidelines. METHODS: The Nursing Role Effectiveness model was used to guide the selection of variables for investigation. Data were collected from administrative records on percent of visits made by Registered Nurses (RN), total number of nursing visits, and consistency of visits by principal nurse. Charts audits were used to collect data on nursing interventions and client outcomes. The sample consisted of 338 nurses from 13 home care offices and 939 de-identified client charts. Hierarchical generalized linear regression approaches were constructed to explore which variables explain variation in client outcomes. RESULTS: The study found documentation of nursing interventions based on best practice guidelines was positively associated with improvement in dyspnea, pain, falls, and pressure ulcer outcomes. Percent of visits made by an RN and consistency of visits by a principal nurse were not found to be associated with improved client outcomes, but the total number of nursing visits was. LINKING EVIDENCE TO ACTION: Implementation of best practice is associated with improved client outcomes in the home care setting. Future research needs to explore ways to more effectively foster the documentation of evidence-based practice interventions.
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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 | 0.011 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".