Undergraduate nursing students’ attitudes towards the care of dying patients
Bibliographic record
Abstract
Background and objectives: A holistic approach to care is essential as nurses must aim to address all aspects of a patient to provide the best quality and most personalized care. Regardless of nursing units or patients’ situations, dying is inevitable, and nursing students may need to provide care for the dying patient during pre-licensure preparation. Studies show nursing students are ill prepared to provide this care. This study aims to understand nursing students’ attitudes towards and perceived emotional preparation to care for dying patients at an undergraduate baccalaureate program in the northeast.Methods: The study utilized a mixed methods approach of analyzing the electronic survey distributed via Qualtrics to all nursing students in an undergraduate nursing program at a mid-size public university in northern New Jersey. The study utilized the Frommelt’s Attitudes Toward Care of Dying Scale with results analyzed through one-way ANOVA testing and regression tests utilizing SPSS software. Thematic analysis was used to analyze the answers to the open-ended questions.Results and conclusion: Sixty (N = 60) students completed the survey. Data analysis revealed a significant relationship between students with previous death experience and education through regression testing (p < .05). Themes included feeling unprepared and afraid of patient death and desiring to contribute positively to the dying patients’ experience. Results of this study may be used to encourage further discussion on the emotional aspects of nursing care for dying patients. Strategies can be implemented in undergraduate programs to enhance students’ emotional abilities to provide care during these distressing circumstances.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".