Patients with heart failure and their partners with chronic illness: interdependence in multiple dimensions of time
Bibliographic record
Abstract
BACKGROUND: Informal caregivers play a vital role in supporting patients with heart failure (HF). However, when both the HF patient and their long-term partner suffer from chronic illness, they may equally suffer from diminished quality of life and poor health outcomes. With the focus on this specific couple group as a dimension of the HF health care team, we explored this neglected component of supportive care. MATERIALS AND METHODS: From a large-scale Canadian multisite study, we analyzed the interview data of 13 HF patient-partner couples (26 participants). The sample consisted of patients with advanced HF and their long-term, live-in partners who also suffer from chronic illness. RESULTS: The analysis highlighted the profound enmeshment of the couples. The couples' interdependence was exemplified in the ways they synchronized their experience in shared dimensions of time and adapted their day-to-day routines to accommodate each other's changing health status. Particularly significant was when both individuals were too ill to perform caregiving tasks, which resulted in the couples being in a highly fragile state. CONCLUSION: We conclude that the salience of this couple group's oscillating health needs and their severe vulnerabilities need to be appreciated when designing and delivering HF team-based care.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| 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".