An evaluation of the hierarchical factor structure of the Persian-translated death anxiety scale in nursing students of Iran
Bibliographic record
Abstract
Background/Objective: Clinical nurse educators globally have recognized the prominent necessity of evaluating for death anxiety in students, and adopting curriculum that provides education about death and dying. Reliable assessment tools are needed to evaluate death anxiety in the student population. The study evaluates the hierarchical factor structure of the Persian-translated Templer’s Death Anxiety Scale (TDAS) in nursing students from Iran.Methods: A repeated measures standard psychometric analysis was conducted. In total 400 undergraduate and graduate nursing students from a major university campus in Sari, Iran finished the Persian translated 15-item TDAS. Construct validity was assessed. Reliability was tested using Cronbach’s Alpha (α), Theta (θ), and McDonald’s Omega (Ω) coefficients.Results: Exploratory factor analysis (N = 200) indicated the TDAS had two factors (Fear of loss of life; Fear to face death). Model fitness indicators confirmed two independent TDAS structure levels. The Cronbach’s alpha, Theta, McDonald, and construct reliability were larger than .70.Conclusions: Study outcomes corroborated acceptable psychometric properties and factor structure for the TDAS in a sample of Iranian nursing students. Findings suggest that the scale can be utilized for reliable and valid educational evaluation of death anxiety in Iranian nursing students.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".