Psychometric evaluation of the anticipatory grief scale in a sample of family caregivers in the context of palliative care
Bibliographic record
Abstract
INTRODUCTION: In palliative care, family caregivers are often faced with experiences of grief in anticipation of the loss of a close person. An instrument designed to measure this form of grief is the Anticipatory Grief Scale, which includes 27 items and has been used in several studies in various contexts. However, the instrument has not been validated. AIM: The aim was to evaluate the psychometric properties, focusing on the factor structure, of the Anticipatory Grief Scale in a sample of family caregivers in palliative care. METHODS: The study had a cross-sectional design. Data were collected from an intervention study in palliative home care that took place between 2013 and 2014. In total, 270 family caregivers in palliative care completed a baseline questionnaire, including the Anticipatory Grief Scale. The factor structure of the scale was evaluated using exploratory factor analysis. RESULTS: The initial factor analysis suggested a four-factor solution, but, due to weak communalities, extensive crossloadings, and item inconsistencies, the model was problematic. Further analysis supported that the scale should be reduced to 13 items and two factors. The two subscales captured the behavioral and emotional reactions of grief in family caregivers in palliative care and were named Behavioral reactions and Emotional reactions. This modified version will hereafter be named AGS-13. CONCLUSIONS: This validation study of the Anticipatory Grief Scale resulted in a revised two-factor model, AGS-13, that appears to be promising for use in palliative care but needs to be tested further.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".