Profile of mild behavioral impairment and factor structure of the Mild Behavioral Impairment Checklist in cognitively normal older adults
Bibliographic record
Abstract
OBJECTIVES: In this large population study, we set out to examine the profile of mild behavioral impairment (MBI) by using the Mild Behavioral Impairment Checklist (MBI-C) and to explore its factor structure when employed as a self-reported and informant-rated tool. DESIGN: This was a population-based cohort study. SETTING: Participants were recruited from the Platform for Research Online to Investigate Genetics and Cognition in Aging study (https://www.protect-exeter.org.uk). PARTICIPANTS: A total of 5,742 participant-informant dyads participated in the study. MEASUREMENTS: Both participants and informants completed the MBI-C. The factor structure of the MBI-C was evaluated by exploratory factor analysis. RESULTS: The most common MBI-C items, as rated by self-reported and informants, related to affective dysregulation (mood/anxiety symptoms), being present in 34% and 38% of the sample, respectively. The least common items were those relating to abnormal thoughts and perception (psychotic symptoms) (present in 3% and 6% of the sample, respectively). Only weak correlations were observed between self-reported and informant-reported MBI-C responses. Exploratory factor analysis for both sets of respondent answers indicated that a five-factor solution for the MBI-C was appropriate, reflecting the hypothesized structure of the MBI-C. CONCLUSION: This is the largest and most detailed report on the frequency of MBI symptoms in a nondementia sample. The full spectrum of MBI symptoms was present in our sample, whether rated by self-reported or informant report. However, we show that the MBI-C performs differently in self-reported versus informant-reported situations, which may have important implications for the use of the questionnaire in clinic and research.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".