A preliminary investigation of moral reasoning and empathy after traumatic brain injury in adolescents
Bibliographic record
Abstract
PRIMARY OBJECTIVE: Traumatic brain injury (TBI) sustained during childhood can affect a number of socio-cognitive skills; however, little attention has focused on the integrity of moral reasoning in the assessment of post-TBI social sequelae and the role of empathy and intelligence on moral maturity. RESEARCH DESIGN: In a quasi-experimental, cross-sectional research design, moral reasoning maturity and empathy in adolescents with mild-to-severe TBI (n = 25) were compared to typically-developing peers (n = 66). METHODS AND PROCEDURES: Participants were administered the So-Moral and So-Mature, tasks of socio-moral reasoning and maturity, the Index of Empathy for Children and Adolescents, the Wechsler Abbreviated Scale of Intelligence and a demographic questionnaire. MAIN OUTCOMES AND RESULTS: Participants with TBI had significantly lower levels of moral reasoning maturity. Further, adolescents with moderate-to-severe TBI had lower levels of empathy. Empathy correlated positively with moral reasoning abilities and, together with intellectual function, predicted a small, but significant proportion of moral reasoning outcome. CONCLUSIONS: Youth who sustained TBI during childhood have poorer moral reasoning abilities than their non-injured peers, potentially placing them at risk for poor social decision-making and socially maladaptive behaviour. This can have a significant impact on long-term social functioning.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".