Relationship Between Neurocognitive Function and Self-Discrepancy After Severe Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: Individuals with traumatic brain injury (TBI) often make unfavorable comparisons between their preinjury and postinjury selves. Although it is known that negative "self-discrepancy" is related to poorer emotional adjustment, the impact of neurocognitive function on self-discrepancy is unclear. This study aimed to investigate the relationship between current neurocognitive function (attention, memory, executive function, and self-awareness) and self-discrepancy after severe TBI. DESIGN: Fifty-four adults with severe TBI were recruited from outpatient and community rehabilitation services and completed measures of self-discrepancy (Head Injury Semantic Differential Scale-III) and self-awareness (Awareness Questionnaire) and a battery of standardized tests of attention, memory, and executive functions. RESULTS: More negative self-discrepancy was significantly associated with greater self-awareness (r = -0.40, P < .001) and better performance on tests of immediate memory (r = -0.43, P < .01), working memory (r = -0.35, P < .05), and verbal fluency (r = -0.34, P < .05). Self-awareness was the only neurocognitive factor significantly and uniquely related to self-discrepancy, accounting for 8.6% of the variance (P < .05). Furthermore, self-awareness was found to partially mediate the relationship between immediate memory and self-discrepancy. CONCLUSION: Better neurocognitive function is related to more negative self-discrepancy. These findings improve understanding of the process of identity reconstruction following TBI.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".