Reliability and Validity of the Chinese Version of Mild Behavioral Impairment Checklist in Mild Cognitive Impairment and Mild Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Mild behavioral impairment (MBI) has been proposed as an early manifestation of dementia. The Mild Behavioral Impairment Checklist (MBI-C) may help identify MBI in prodromal and preclinical dementia. OBJECTIVE: The study aimed to evaluate the reliability and validity of the Chinese version of MBI-C in mild cognitive impairment (MCI) and mild Alzheimer's disease (AD), and to explore the structure of the five factors of the MBI-C in Chinese culture. METHODS: Sixty dyads of MCI and mild AD (MCI, n = 33; mild AD, n = 35) were recruited. The informants completed the MBI-C and Neuropsychiatric Inventory Questionnaire (NPI-Q) and were interviewed for clinician rating of the NPI. The Cronbach's coefficient was used to measure the structural reliability of the MBI-C. The criterion-validity was evaluated with the correlation coefficient between the MBI-C and the total scores of NPI-Q and NPI. Exploratory factor analysis was conducted to investigate the structure of the MBI-C. RESULTS: The Cronbach's α coefficient was 0.895. The MBI-C total score was positively correlated with all five domains (r = 0.577∼0.840). The total score of MBI-C was significantly correlated with the total scores of NPI-Q (r = 0.714) and NPI (r = 0.749). Similarly, the five domain scores of MBI-C were significantly correlated with the factor and total scores of NPI-Q (r = 0.312∼0.673) and NPI (r = 0.389∼0.673). The components of each factor in Chinese version of MBI-C were slightly different from those of the a priori defined domains (χ2 = 1818.202, df = 496, p < 0.001). CONCLUSION: The Chinese version of MBI-C has good reliability and validity, and can be used to evaluate the psychological and behavioral changes in MCI and mild AD.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".