Attitudes toward caring for dying patients: An overview among Italian nursing students and preliminary psychometrics of the FATCOD-B scale
Bibliographic record
Abstract
Background : Extensive research indicated that nursing students at the end of their curricula feel unprepared to care for the dying. Therefore, studies on nursing students’ attitudes to caring for dying patients play a key role in nursing education, and the FATCOD-B scale is the only tool that specifically evaluates them. Methods : A cross-sectional study design was used. During 2013, the FATCOD-B scale was administered to a sample of 82 nursing students. Descriptive statistics, the Student’s t -test, Cronbach’s alpha and principal components analysis were used in data analyses. Results: Italian nursing students’ scores on the FATCOD-B scale seemed to be lower than literature evidence on sums of ratings; however, mean ratings revealed similar levels of attitudes to the dying. Gender and personal experience with dying persons were not predictors of FATCOD-B scores. Internal consistency of the FATCOD-B scale was not satisfactory. Principal component analysis found a four-factor questionable solution for the FATCOD-B scale. Conclusions: Findings from the present study suggest a need for end-of-life care nursing education in Italy. The preliminary results concerning the psychometrics of the scale suggest that the Italian version of the FATCOD-B could be in need of revision. The implications for practice are discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".