Health-Related Quality of Life in Heart Failure Patients With Varying Levels of Health Literacy Receiving Telemedicine and Standardized Education
Bibliographic record
Abstract
The purpose of this study was to examine the effect of telemonitoring plus education by home healthcare nurses on health-related quality of life in patients with heart failure who had varying health literacy levels. In this pretest/posttest treatment only study, 35 patients with a diagnosis of heart failure received home healthcare nurse visits, including education and telemonitoring. Heart failure education was provided by nurses at each home healthcare visit for approximately 15 to 20 minutes. All participants completed the Short-Form Test of Functional Health Literacy in Adults (S-TOFHLA) and the Minnesota Living with Heart Failure Questionnaire (MLHFQ) during the first week of home healthcare services. The MLHFQ was administered again at the completion of the covered home healthcare services period (1-3 visits per week for 10 weeks). Most participants were older adults (mean age 70.91±12.47) and had adequate health literacy (51.4%). Almost half of the participants were NYHA Class III (47.1%). All participants received individual heart failure education, but this did not result in statistically significant improvements in health-related quality-of-life scores. With telemonitoring and home healthcare nurse visits, quality-of-life scores improved by the conclusion of home healthcare services (clinically significant), but the change was not statistically significant. Individuals with marginal and inadequate health literacy ability were able to correctly use the telemonitoring devices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".