Bibliographic record
Abstract
Heart failure (HF) is a frequently encountered illness in primary care settings with a one-year mortality rate of up to 75%. HF management appears to focus on maximizing heart function rather than on quality of life. Recently, the Canadian Hospice Palliative Care Association proposed the utilization of an integrated palliative approach when caring for patients with chronic life-limiting conditions. A palliative approach applies the principles of palliative care earlier in the course of a life-limiting disease, whenever there are unmet needs. The purpose of this project was to answer the question, “How can the Family Nurse Practitioner (FNP) apply a palliative approach in the care of individuals living with heart failure”? An analysis of the literature revealed that symptom burden and communication were significant areas of concern. Through the application of a palliative approach to these key areas, the FNP can positively influence the quality of living and dying for HF patients. Implications for FNP clinical practice, research and education are also identified and include the development of a comprehensive assessment tool for HF symptoms, early initiation of advanced care planning, and greater research and education regarding the effectiveness of a palliative approach.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".