Evaluating Decision-Making: Validation and Regression-Based Normative Data of the Judgment Assessment Tool
Bibliographic record
Abstract
OBJECTIVE: This study presents the results of the development and validation of the Judgment Assessment Tool (JAT). The JAT measures two core aspects of judgment, namely generation of solutions (G) and assessment of options (A), the two first stages of decision-making process. METHOD: During the test development phase (study 1), a preliminary version of the JAT was evaluated by 14 experts and tested on 30 healthy controls (HC). One hundred and twenty HC (20-84 years old) and 24 participants with mild Alzheimer's disease (AD) were subsequently tested on the final version of the JAT (study 2). HC participants aged 60 and over and AD participants underwent a neuropsychological evaluation. RESULTS: The internal consistency of the final version of the JAT assessed by Cronbach's a was 0.71 for the HC group and 0.85 for the AD group. Performance on the JAT was normally distributed both in the HC and AD groups. The test correlated with abstract reasoning, verbal fluency, and working memory. Results revealed adequate test-retest reliability and excellent interrater reliability (k coefficient was 0.92 for the G section and 0.93 for the A section). Demographically adjusted normative data were generated based on a regression analysis and results showed that AD participants performed worse than HC with a large effect size (Cohen's d = 1.79). CONCLUSION: Overall, these results provide evidence of the reliability and strong construct validity of the JAT to evaluate judgment.
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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 | 0.040 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".