Evidence-based Standardized Care Plans for Use Internationally to Improve Home Care Practice and Population Health
Bibliographic record
Abstract
OBJECTIVES: To develop evidence-based standardized care plans (EB-SCP) for use internationally to improve home care practice and population health. METHODS: A clinical-expert and scholarly method consisting of clinical experts recruitment, identification of health concerns, literature reviews, development of EB-SCPs using the Omaha System, a public comment period, revisions and consensus. RESULTS: Clinical experts from Canada, the Netherlands, New Zealand, and the United States participated in the project, together with University of Minnesota School of Nursing graduate students and faculty researchers. Twelve Omaha System problems were selected by the participating agencies as a basic home care assessment that should be used for all elderly and disabled patients. Interventions based on the literature and clinical expertise were compiled into EB-SCPs, and reviewed by the group. The EB-SCPs were revised and posted on-line for public comment; revised again, then approved in a public meeting by the participants. The EB-SCPs are posted on-line for international dissemination. CONCLUSIONS: Home care EB-SCPs were successfully developed and published on-line. They provide a shared standard for use in practice and future home care research. This process is an exemplar for development of evidence-based practice standards to be used for assessment and documentation to support global population health and research.
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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 | 0.332 | 0.468 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".