Psychometric Properties of a Modified Version of the Caregiver Reaction Assessment Scale Measuring Caregiving and Post-Caregiving Reactions of Caregivers of Cancer Patients
Bibliographic record
Abstract
Most care received by cancer patients is provided in the community by informal or unpaid caregivers. The unrelenting care demands can lead to physical, emotional, social, and financial reactions; furthermore, studies indicate that the effects of caregiving may endure after the patient's death. A need therefore exists for instruments measuring both caregiving and post-caregiving reactions. Among available instruments, the Caregiver Reaction Assessment (CRA) is a multidimensional, 5-factor measure designed to assess the negative and positive aspects of caregiving. The current study examined the psychometric properties and factor structure of responses to a modified Hebrew version of the CRA aimed at measuring caregiving and post-caregiving reactions. Although the scale was modified, it was assumed that, similar to the original CRA, a 5-factor structure would be supported by means of confirmatory factor analysis. A total of 236 bereaved primary caregivers of cancer patients from central and southern regions of Israel were recruited over a period of 18 months. As hypothesized, results provide support for a 5-factor structure of responses to this modified version of the CRA. The concurrent validity of responses to the scale was also supported. Replication of the findings with randomly derived and larger sample sizes is needed.
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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 | 0.005 | 0.018 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".