The construct of problem solving in higher level neuropsychological assessment and rehabilitation*1
Bibliographic record
Abstract
Three inter-related studies examine the construct of problem solving as it relates to the assessment of deficits in higher level outpatients with traumatic brain injury (TBI). Sixty-one persons with TBI and 58 uninjured participants completed measures of problem solving and conceptually related constructs, which included neuropsychological tests, self-report inventories, and roleplayed scenarios. In Study I, TBI and control groups performed with no significant differences on measures of memory, reasoning, and executive function, but medium to large between-group differences were found on timed attention tasks. The largest between-group differences were found on psychosocial and problem-solving self-report inventories. In Study II, significant-other (SO) ratings of patient functioning were consistent with patient self-report, and for both self-report and SO ratings of patient problem solving, there was a theoretically meaningful pattern of correlations with timed attention tasks. In Study III, a combination of self-report inventories that accurately distinguished between participants with and without TBI, even when cognitive tests scores were in the normal range, was determined. The findings reflect intrinsic differences in measurement approaches to the construct of problem solving and suggest the importance of using a multidimensional approach to assessment.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 | 0.000 |
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".