Gender Differences in the Influence of Social Support on One-Year Changes in Functional Status in Older Patients with Heart Failure
Bibliographic record
Abstract
The purpose of this study was to determine the combined effects of gender and levels of social support on 1-year functional health outcomes in older persons diagnosed with heart failure (HF). Persons ≥ 65 years of age with an acute HF exacerbation (164 females; 271 males) were enrolled and followed for a year. Participants completed baseline and 12-month questionnaires containing clinical and demographic descriptive information and validated self-report measures of: (1) physical functioning (Medical Outcome Study [MOS] SF12 and Kansas City Cardiomyopathy Questionnaire [KCCQ]) and (2) social support (MOS- Social Support Survey). Women were more likely to be single, widowed or divorced, living alone and earning less annual income. At baseline, women reported significantly lower support and physical function scores. However, at 1 year there were no significant gender differences in the proportion of men or women who experienced clinically meaningful functional decline or death across the year of follow-up. In multivariable modeling, men with lower levels of social support were more likely to experience functional decline. This was not the case for women. Our findings suggest that gender-directed strategies to promote optimization of function for both men and women living with HF in their community are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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) | 0.002 | 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".