Death attitudes and positive coping in Spanish nursing undergraduates: a cross‐sectional and correlational study
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To analyse the relationship between death attitudes, emotional intelligence, resilience and self-esteem in a sample of nursing undergraduates. BACKGROUND: The death attitudes held by nursing students may influence the care they offer to end-of-life patients and their families. Emotional intelligence, resilience and self-esteem are important social and emotional competencies for coping positively with death and dying. DESIGN: Cross-sectional and correlational study. METHODS: Participants were 760 nursing undergraduates from four nursing schools in Spain. Data were collected in 2013-2014. The students responded anonymously to a self-report questionnaire that gathered socio-demographic data and which assessed the following aspects: fear of death (Collett-Lester Fear of Death Scale), death anxiety (Death Anxiety Inventory-Revised), perceived emotional intelligence (Trait Meta-Mood Scale, with its three dimensions: attention, clarity and repair), resilience (Brief Resilient Coping Scale) and self-esteem (Rosenberg Self-Esteem Scale). In addition to descriptive statistics, analyses of variance, mean differences, correlations and regression analyses were computed. RESULTS: Linear regression analysis indicated that attention to feelings, resilience and self-esteem are the significant predictors of death anxiety. CONCLUSIONS: The results show that death anxiety and fear of death are modulated by social and emotional competencies associated with positive coping. RELEVANCE TO CLINICAL PRACTICE: The training offered to future nurses should include not only scientific knowledge and technical skills but also strategies for developing social and emotional competencies. In this way, they will be better equipped to cope positively and constructively with the suffering and death they encounter at work, thus helping them to offer compassionate patient-centred care and minimising the distress they experience in the process.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".