Psychometric Properties of the UCSF Fein MAC Educational & Developmental History Questionnaire: A Novel Screening Tool for Capturing Early Life Learning Profiles Across Healthy Aging and Dementia Populations
Bibliographic record
Abstract
Background: Increasing evidence suggests that neurodevelopmental differences substantially alter the expression and course of later-life neurodegenerative diseases. Standard approaches for determining early-life neurodevelopmental differences in aging populations rely largely on chart-based reviews, which poses a methodological challenge due to the varied quality and completeness of medical records. To overcome this limitation, we created the novel Educational & Developmental History (EDevHx) form, a retrospective questionnaire designed to capture early developmental features. Here, we evaluated its psychometric properties among a large sample of cognitively unimpaired, aging adults. Methods: The EDevHx was completed by 677 clinically normal adults aged 46-95 years who underwent standard evaluations to establish their cognitively healthy status. Results: EDevHx items grouped into hypothesized domains (Language, Motor, Visuospatial/Mathematical, Attention, Social) significantly loaded onto their associated domains via confirmatory factor analysis. For each factor, the associated items significantly related to the factor while holding other items constant, indicating a lack of redundancy. Multidimensional scaling analysis showed items were visually grouped within hypothesized domains. Each factor demonstrated acceptable internal consistency. Test-retest reliability ranged from moderate to good, except for the Social factor's, which was poor. Each factor (as well as two items theorized not to map onto any hypothesized domain) demonstrated convergent/divergent validity with validated questionnaires/neurocognitive tests. Conclusion: The EDevHx tool represents an easily scalable and robust method for capturing early developmental features among aging populations. The present study demonstrates its strong psychometric properties, supporting its immediate and widespread integration into clinical and research practices alike.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".