Two prelicensure nursing programs assess readiness to standardize palliative and end of life care curriculum
Bibliographic record
Abstract
There is a growing imperative for nurses to be adequately trained to care for patients with serious, life-limiting illness. However, the current nursing education system requires vast content areas be taught, resulting in minimal emphasize on palliative and end-of-life care and inadequate preparation of nurses to care for dying patients upon entering practice. To address the need for enhanced palliative and end-of-life care integration within their respective programs, two universities conducted needs assessments to determine the best next steps in enhancing student preparation to care for patients with serious, life-limiting illness. One university engaged in a three-part needs assessment resulting in the formation of an ad hoc committee to guide discussions for content integration. The second university engaged in a faculty-led survey to identify areas for improvement within the program. The purpose of this paper is to describe the processes and challenges encountered by both schools to aid other programs that may be considering or preparing for a similar endeavor.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".