Factor Analysis of the 12-Item Zarit Burden Interview in Caregivers of Persons Diagnosed With Dementia
Bibliographic record
Abstract
The Zarit Burden Interview (ZBI) is commonly used to measure dementia caregiver burden, but its factor structure is unclear. A two-factor structure for the 12-item ZBI, "personal strain" and "role strain," has been shown, but recent data suggest that an additional factor of "guilt" is embedded in the "role strain" items. The 12-item ZBI administered to 194 informal rural and urban caregivers of persons diagnosed with dementia was analyzed using exploratory factor analysis. A two-factor structure, with item loadings consistent with previously conceptualized constructs of "personal strain" and "role strain," was found. Moreover, this factor structure was invariant to caregiver subgroups. When the predictive value of these factors was explored, only "personal strain" was important in predicting caregiver psychological distress, measured with the Brief Symptom Inventory. However, "role strain," which included the hypothesized "guilt" items, did not appear to be an important predictor of caregiver distress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| 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) | 0.001 | 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".