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 using the Mild Behavioral Impairment Checklist (MBI-C), and determine its factor structure when employed as a self-report and informant rated tool.Design: Population based cohort study. Setting: Online testing via the PROTECT study (http://www.protectstudy.org.uk)Participants: 5,742 participant-informant dyads.Measurements: Both participants and informants completed the MBI-C. The factor structure of the MBI-C was evaluated by exploratory factor analysis (EFA).Results: The most common MBI-C items as rated by self-report and informants related to affective dysregulation (mood/anxiety symptoms), being present in 34% and 38% of the sample respectively. The least common were items relating to abnormal thoughts and perception (psychotic symptoms) (present in 3 and 6% of the sample respectively). There were only weak correlations between self-report and informant-rated MBI-C responses. EFA for both sets of respondent answers indicated a five-factor solution for the MBI-C was appropriate, reflecting the structure of the MBI-C.Conclusion: This is the largest and most detailed report on the frequency of MBI symptoms in the general population. The full spectrum of MBI symptoms was present in our sample, whether rated by self-report or informant report. However, we show that the MBI-C performs differently in self-rated versus informant-rated situations, which may have important implications for the use of the questionnaire in clinic and research.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".