Educating Nurses for Palliative Care
Bibliographic record
Abstract
An aging population requires that nurses in all areas of practice be knowledgeable about high-quality palliative care. The purpose of this scoping review was to summarize the available evidence for providing palliative care education for nurses. Searches were conducted in the spring of 2012 of 5 electronic databases using controlled vocabulary. English-language articles published between 2001 and 2011 were included in the review, yielding a sample of 58 studies. Findings reviewed included country and setting of study; palliative knowledge taught; methods, number of hours, and duration of education; study design; and evaluation methods. Eighty-six percent of studies reported positive outcomes. Effect size calculations for 9 outcome measures resulted in large (n = 1), moderate (n = 4), and small (n = 4) effects in a positive direction. However, effect sizes were heterogeneous, suggesting moderator variables. Although there appears to be an overall positive effect of palliative education, findings from this scoping review illustrate the diversity of educational approaches and lack of rigorous study designs, making it difficult to make recommendations for an evidence-based approach to educating nurses in palliative care.
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 | 0.018 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".