Attitudes towards death and dying among intensive care professionals: A cross-sectional design evaluating culture-related differential item functioning of the frommelt attitudes toward care of the dying instrument
Bibliographic record
Abstract
Objective: The objective is to examine whether one of the most used instruments for measuring attitudes towards caring for dying patients, the Frommelt Attitude Toward Care of the Dying (FATCOD-B) instrument, has the same meaning across different societal contexts, as exemplified by Swedish and Saudi Arabian intensive care professionals. Methods: A cross-sectional design used the 30-item FATCOD-B questionnaire. It was distributed to intensive care professionals from Sweden and Saudi Arabia, generating a total sample of 227 participants. Ordinal logistic regression models were used to examine the differential item functioning (DIF) for each item. Results: Up to 12 of the 30 items were found to have significant DIF values related to: (a) Swedish and Saudi Arabian intensive care professionals, (b) Swedish and Saudi Arabian registered nurses (RNs), (c) RNs' levels of experience and (d) RNs and other intensive care professionals in Saudi Arabia. Conclusions: The results indicate that FATCOD should be used cautiously when comparing attitudes towards death and dying across different societal and healthcare contexts.
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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 | 0.007 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".