The Measurement of Sociomoral Reasoning in Adolescents With Traumatic Brain Injury: A Pilot Investigation
Bibliographic record
Abstract
Abstract Moral reasoning skills are crucial for appropriate and adaptive social functioning. Impairments in moral reasoning have been associated with aggressive and violent behaviours. Traditional measures of moral reasoning may provide limited insight into daily behavioural functioning as these measures are dependent on several higher order cognitive skills and, as such, may be limited in their utility with certain clinical populations. In Study 1, new measures of sociomoral reasoning and maturity, the So-Moral and So-Mature, were described. Further, the psychometric properties of the So-Moral and So-Mature were investigated in a sample of 50 adolescents aged 11 to 19 (mean age = 14.5,SD= 2.6 years, 25 male). Preliminary support was found for the validity and reliability of both tasks. In Study 2, the clinical applicability of the So-Moral and So-Mature were examined in a sample 25 adolescents with traumatic brain injury (TBI; mean age = 13.8,SD= 2.3 years, 15 male). Response trends suggested that adolescents with TBI generated less developmentally mature responses to sociomoral dilemmas and those with more severe injuries performed most poorly. The sociomoral reasoning tasks described promote participant's emotional involvement and investment as well as being appropriate for use with clinical populations.
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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 | 0.005 |
| 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 | 0.001 |
| 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".