Psychometric Properties of an Abridged Version of the Zarit Burden Interview Within a Representative Canadian Caregiver Sample
Bibliographic record
Abstract
PURPOSE: Given the exponential increase in dementia prevalence anticipated in the coming years, measurement of caregiver burden has become common in gerontological research and clinical practice. The Zarit Burden Interview (BI) has emerged as the most widely utilized burden measure. The current study examines the psychometric properties of responses to an abridged, 12-item version of this scale. DESIGN AND METHODS: Data were derived from a national epidemiological study of dementia incidence and patterns of care (N = 1,095). Informal caregivers of surviving institutionalized and community-dwelling index subjects were interviewed 5 years subsequent to initial recruitment (n = 770). RESULTS: Results of both the exploratory and confirmatory factor analyses support a two-factor structure of responses to this abridged scale. Subsequent to control for demographic variables, dementia illness features, and baseline depressive symptoms at baseline, responses to this brief BI provide a significant increase to prediction of depressive symptoms at Time 2 (R(2) =.24, p <.01) with no additional variance provided by the 10 remaining items from the complete BI (deltaR(2) = 0, ns). IMPLICATIONS: The results of this study are discussed relative to theory and the operational definition of caregiver burden. Findings can be generalized with greater confidence given the representative and national composition of caregivers recruited for this study.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".