Association Between Characteristics of Hospitalized Heart Failure Patients With Their Needs
Bibliographic record
Abstract
INTRODUCTION: During recent years that life expectancy of heart failure patients has been increased, health professionals put more emphasis on assessing their needs in daily clinical practice. The aim of the present study was to explore the association between characteristics of hospitalized heart failure patients with their needs. METHODS: A sample of 190 hospitalized patients with HF, recruited from public hospitals in Greece, was enrolled in the study. Data were collected by the completion of a questionnaire which included socio-demographic and clinical characteristics and the questionnaire "Needs of hospitalized patients with coronary artery disease" which is consisted of 6 subscales. Statistical methods used were Kruskal wallis-test or Mann-Whitney test and Spearmans' rho coefficient. Multiple regression analysis was performed in order to evaluate the association between patients' characteristics and the significance of their needs. RESULTS: 124 (65.3%) of hospitalized heart failure participants were men and 89 (46.8%) of participants were more than 70 years old. 145 (76.3%) had prior experience of hospitalization due to heart failure. The need for support and guidance was statistically significantly associated with the degree of information, (p=<0.001). The need for information from the medical and nursing staff was significantly associated with marital status and degree of information (p=0.001 and p<0.001 respectively). The need for need for being in contact with other patient groups, and ensuring communication with relatives was statistically significantly associated with the professional status and degree of information, (p=0.037 and p=<0.001 respectively). The need for individualized treatment and the need for patient's personal participation to his/her treatment as well as the need to meet the emotional and physical needs were statistically significant associated with the degree of information, (p=<0.001, p=<0.001 respectively). Lastly, the need to trust the medical and nursing staff was statistically significantly associated with the place of residence and the degree of information, (p=0.023 and p<0.001). These results were confirmed by the multiple linear regression after controlling for potential confounders. CONCLUSIONS: Information seems to be of vital importance when assessing the needs of heart failure patients. Therefore, providing elaborate information should be an integral part of their therapeutic regimen.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".