Adaptation and analysis of psychometric features of the Caregiver Risk Screen: a tool for detecting the risk of burden in family caregivers
Bibliographic record
Abstract
BACKGROUND: There are a limited number of scales available in the Spanish language that can be used to detect burden among individuals who care for a dependent family member. The purpose of this work was to adapt and validate the Caregiver Risk Screen (CRS) scale developed by Guberman et al. (2001) (Guberman, N., Keefe, J., Fancey, P., Nahmiash, D. and Barylak, L. (2001). Development of Screening and Assessment Tools for Family Caregivers: Final Report. Montreal, Canada: Health Transition Fund). METHODS: The sample was made up of 302 informal caregivers of dependent family members (average age 57.3 years, and 78.9% were women). Scale structure was subjected to a confirmatory factor analysis. Concurrent and convergent validity were assessed by correlation with validated questionnaires for measuring burden (Zarit Burden Inventory (ZBI)) and psychological health (SCL-90-R). RESULTS: The results show a high level of internal consistency (Cronbach's alpha = 0.86), suitable fit of the one-dimensional model tested via confirmatory factor analysis (GFI = 0.91; CFI = 0.91; RMSEA = 0.097), and appropriate convergent validity with similar constructs (r = 0.77 with ZBI; and r-values between 0.45 and 0.63 with SCL-90-R dimensions). CONCLUSIONS: The findings are promising in terms of their adaptation of the CRS to Spanish, and the results enable us to draw the conclusion that the CRS is a suitable tool for assessing and detecting strain in family caregivers. Nevertheless, new research is required that explores all the psychometric features on the scale.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".