The Zarit Burden Interview
Bibliographic record
Abstract
PURPOSE: The purpose of the study was to develop a short and a screening version of the Zarit Burden Interview (ZBI) that would be suitable across diagnostic groups of cognitively impaired older adults, and that could be used for cross-sectional, longitudinal, and intervention studies. DESIGN AND METHODS: We used data from 413 caregivers of cognitively impaired older adults referred to a memory clinic. We collected information on caregiver burden with the 22-item ZBI, and information about dependence in activities of daily living (ADLs) and the frequency of problem behaviors among care recipients. We used factor analysis and item-total correlations to reduce the number of items while taking into consideration diagnosis and change scores. RESULTS: We produced a 12-item version (short) and a 4-item version (screening) of the ZBI. Correlations between the short and the full version ranged from 0.92 to 0.97, and from 0.83 to 0.93 for the screening version. Correlations between the three versions and ADL and problem behaviors were similar. We further investigated the behavior of the short version with a two-way analysis of variance and found that it produced identical results to the full version. IMPLICATIONS: The short and screening versions of the ZBI produced results comparable to those of the full version. Reducing the number of items did not affect the properties of the ZBI, and it may lead to easier administration of the instrument.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".