Improving Palliative Care Knowledge of nurses caring for heart failure patients
Bibliographic record
Abstract
BACKGROUND: Approximately 80% of patients with advanced heart failure (HF) die within five years of diagnosis and may benefit from palliative care (PC). PC is underutilized in HF patients. One barrier is nurses' insufficient knowledge of PC. This quality improvement project aimed to enhance the PC knowledge of nurses caring for patients with HF in a Canadian tertiary care setting. METHOD: This project was guided by the Knowledge-to-Action framework. Semi-structured interviews were conducted to identify nurses' learning needs, which informed the development of the educational sessions. These sessions were delivered using hybrid, virtual, and asynchronous modalities. PC knowledge tests were used pre- and post-intervention to evaluate the nurses' PC knowledge. The data were presented using descriptive statistics. RESULTS: Thirteen nurses attended the educational sessions. Ten responses were received for both the pre- and post-knowledge tests. Most participants had more than 10 years of experience, were 41 years or older, and had received prior PC training. The post-test showed improved knowledge (90-100%) of opioid use for symptomatic relief of dyspnea, advanced care planning (ACP) discussions, and communication processes. Knowledge of NSAID use in patients with HF increased by 60%. All nurses demonstrated an understanding of ACP concepts before and after the education. PC concept understanding increased from 80 to 90%. CONCLUSIONS: Educational sessions improved nurses' PC knowledge, and future education should emphasize improving PC perceptions and symptom management. However, evaluating the effectiveness of PC education is challenging because of low participation. Further research with a larger sample, longer implementation time, ongoing evaluation of PC knowledge, and nurses with diverse ages and experiences is required to understand the impact of PC education.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".