German version of the Death Attitudes Profile- Revised (DAP-GR) – translation and validation of a multidimensional measurement of attitudes towards death
Bibliographic record
Abstract
BACKGROUND: In Germany, only limited data are available on attitudes towards death. Existing measurements are complex and time consuming, and data on psychometric properties are limited. The Death Attitude Profile- Revised (DAP-R) captures attitudes towards dying and death. The measure consists of 32 items, which are assigned to 5 dimensions (Fear of Death, Death Avoidance, Neutral Acceptance, Approach Acceptance, Escape Acceptance). It has been translated and tested in several countries, but no German version exists to date. This study reports the translation of the Death Attitudes Profile-Revised (DAP-R) into German (DAP-GR) using a cross-cultural adaption process methodology and its psychometric assessment. METHODS: The DAP-R was translated following guidelines for cultural adaption. A total of 216 medical students of the Heinrich Heine University Duesseldorf participated in this study. Interrater reliability was investigated by means of Kendall's W concordance coefficient. The internal consistency of the DAP-GR Scales was assessed with Cronbach's alpha coefficients. Split-half reliability was estimated using Spearman-Brown coefficients. Convergent validity was measured by Spearman's correlation coefficient. Content validity was assessed by means of confirmatory factor analysis (CFA). All statistical analyses were performed using SPSS 24 and AMOS 22. RESULTS: The items showed fair to good interrater reliability, with W-values ranging from .30 to .79. Internal consistency of the five subscales ranged from .61 (Neutral Acceptance) to .94 (Approach Acceptance). Split-half reliability was good, with a Spearman-Brown-coefficient of .83. The results of CFA slightly diverged from the original scale. CONCLUSION: Our results suggest overall good reliability of the German version of the DAP-R. The DAP-GR promises to be a robust instrument to establish normative data on death attitudes for use in German-speaking countries.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".