Managing Heart Failure in the Long-Term Care Setting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Implementation of heart failure guidelines in long-term care (LTC) settings is challenging. Understanding the conditions of nursing practice can improve management, reduce suffering, and prevent hospital admission of LTC residents living with heart failure. OBJECTIVE: The aim of the study was to understand the experiences of LTC nurses managing care for residents with heart failure. METHODS: This was a descriptive qualitative study nested in Phase 2 of a three-phase mixed methods project designed to investigate barriers and solutions to implementing the Canadian Cardiovascular Society heart failure guidelines into LTC homes. Five focus groups totaling 33 nurses working in LTC settings in Ontario, Canada, were audiorecorded, then transcribed verbatim, and entered into NVivo9. A complex adaptive systems framework informed this analysis. Thematic content analysis was conducted by the research team. Triangulation, rigorous discussion, and a search for negative cases were conducted. Data were collected between May and July 2010. RESULTS: Nurses characterized their experiences managing heart failure in relation to many influences on their capacity for decision-making in LTC settings: (a) a reactive versus proactive approach to chronic illness; (b) ability to interpret heart failure signs, symptoms, and acuity; (c) compromised information flow; (d) access to resources; and (e) moral distress. DISCUSSION: Heart failure guideline implementation reflects multiple dynamic influences. Leadership that addresses these factors is required to optimize the conditions of heart failure care and related nursing practice.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it