Influence of Negative Stereotypes and Beliefs on Neuropsychological Test Performance in a Traumatic Brain Injury Population
Bibliographic record
Abstract
The impact of stereotype threat and self-efficacy beliefs on neuropsychological test performance in a clinical traumatic brain injury (TBI) population was investigated. A total of 42 individuals with mild-to-moderate TBI and 42 (age-, gender-, educationally matched) healthy adults were recruited. The study consisted of a 2 (Type of injury: control, TBI) × 2 (Threat Condition: reduced threat, heightened threat) between-participants design. The purpose of the reduced threat condition was to reduce negative stereotyped beliefs regarding cognitive effects of TBI and to emphasize personal control over cognition. The heightened threat condition consisted of an opposing view. Main effects included greater anxiety, motivation, and dejection but reduced memory self-efficacy for head-injured-groups, compared to control groups. On neuropsychological testing, the TBI-heightened-threat-group displayed lower scores on Initial Encoding (initial recall) and trended toward displaying lower scores on Attention (working memory) compared to the TBI-reduced-threat-group. No effect was found for Delayed Recall measures. Memory self-efficacy mediated the relation between threat condition and neuropsychological performance, indicating a potential mechanism for the threat effect. The findings highlight the impact of stereotype threat and self-referent beliefs on neuropsychological test performance in a clinical TBI population.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".