Closing the Gap Between Evidence and Action: How Outcome Measurement Informs the Implementation of Evidence-based Wound Care Practice in Home Care.
Bibliographic record
Abstract
UNLABELLED: Measured outcomes can help assure successful implementation of evidence-based wound care programs by informing patients, professionals, and payors that a health care system is both efficient and effective. OBJECTIVE: Illustrate how clinical and economic outcome measurement was important to ensure sustainability of standardized evidence-based wound care programs implemented in Canadian community care. METHODS: Client assessments, dressing change frequency, wound healing, and economic outcomes were measured on 16,079 Canadian home care clients, including 8089 with a total of 11,160 chronic or acute wounds during standardized evidence-based protocol implementation that involved education, knowledge transfer, strategic planning, management accountability/receptivity, communication, and either prospective client assessment-based data or retrospective chart audit data to measure outcomes. RESULTS: Results from 3 regions illustrate how evidence-based protocol use decreased length of service, dressing change frequency, wound care costs, and wound closure time. Client and staff empowerment and management involvement were among key factors for success. CONCLUSION: Objectively measuring and reporting outcomes provided a concrete context for increasing organizational efforts to improve wound care practices and provided a solid foundation for sustained evidence-based protocol usage as it allowed agencies to track improvement in health and economic outcomes.
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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 | 0.551 | 0.675 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".