A structured home visit program by non-licensed healthcare personnel can make a difference in the management and readmission of heart failure patients
Bibliographic record
Abstract
The purpose of this observational study is to evaluate the effectiveness of structured home visits by non-licensed healthcare personnel (NLHP) on the patient’s adherence to a medication plan, dietary restrictions, and knowledge about when to seek care. This descriptive, qualitative study evaluates the Grand-Aide ® program that aids in the management of heart failure patients across the continuum of care using NLHP. Patients are offered enrollment in the program starting the day following hospital discharge. Visit frequency is high immediately after discharge and lessens over time as the patient’s health knowledge and condition improve. During the visits, NLHP record vital signs including weight, ask the patient protocol questions in a Yes/No format, record and report responses to the supervising Nurse Practitioner, reinforce discharge teaching, and review medications. Preliminary findings from this project provide information about issues found while providing home-visiting services to patients with heart failure. Early recognition of these issues allows for early treatment or correction, preventing further deterioration that could lead to readmission. Programs like this can be an integral part of the health care system that manages patient’s care across the continuum, with intensive focus immediately after hospital discharge.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".