Mieux saisir les difficultés d’adaptation des personnes âgées insuffisantes cardiaques en transition de l’hôpital vers le domicile à partir des expériences vécues et d’un éclairage théorique
Bibliographic record
Abstract
Heart failure is one of the most common reasons for hospitalization in older people, and the hospital-to-home transition can be unsuccessful for these patients. Existing care programs focus primarily on the physiological aspects of the disease and are rarely based on theory. Using Roy's adaptation model (1), the aim of this study was to develop a thorough understanding of the adaptation difficulties and factors that influence how well elderly patients with chronic heart failure cope with the hospital-to-home transition, in order to develop a nursing interventions program. Based on the process proposed by Sidani and Braden (2011), this qualitative descriptive study adopted a deductive approach, with the use of intermediary theories and empirical data, as well as an inductive approach, where older people with chronic heart failure (n=7), caregivers (n=6), and healthcare professionals (n=14) participated in semi-structured individual interviews. The triangulation of data highlights the difficulties and factors influencing adaptation at the physical, psychological, and social levels. Gaining a better understanding of the experience of older people with heart failure when it comes to their transition from hospital to home, and doing so with a holistic vision, provides information for interventions that can contribute to better management of chronic disease and a better quality of life for these elderly patients.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".