Impact of Partial Administration of the Cognitive Behavioral Driver’s Inventory on Concurrent Validity for People With Brain Injury
Bibliographic record
Abstract
OBJECTIVES: We sought to determine whether the partial administration of the Cognitive Behavioral Driver's Inventory (CBDI) has a significant effect on its concurrent validity. METHOD: Data were extracted from charts of clients with cerebrovascular accident or traumatic brain injury from three centers. The CBDI was administered either completely or partially (right and left perimetry or Wechsler Adult Intelligence Scale-Revised (WAIS-R; Wechsler, 1982; Picture Completion and Digit Symbol tests were not completed). Concurrent validity indicators were calculated for the CBDI and three different scenarios of partial administration of the CBDI. RESULTS: Only 52% of the road test failures were predicted correctly by the completely administered CBDI. Nonadministration of the WAIS-R rarely modified the CBDI results. Omission of perimetry scores tended to increase the sensitivity and decrease the specificity (not significantly). CONCLUSION: The CBDI should be used as a complement, not a substitution, for a road test. Partially administrating the CBDI, specifically excluding perimetry measures, can affect its concurrent validity.
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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 | 0.022 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".