Training Emotional Processing in Persons With Brain Injury
Bibliographic record
Abstract
AIMS: To determine the effectiveness of 2 interventions for different aspects of emotion-processing deficits in adults with acquired brain injury (ABI). PARTICIPANTS: Nineteen participants with ABI (minimum 1 year postinjury) from Western New York and Southern Ontario, Canada. INTERVENTIONS: (1) Emotion processing from faces ("facial affect recognition" or FAR) and (2) emotion processing from written context by using "stories of emotional inference" (SEI). Ten randomly assigned participants received the FAR intervention, and 9 received the SEI protocol. Both interventions were administered 1 hour per day, 3 times per week, and completed in 6 to 9 sessions, and both incorporated participants' personal emotional experiences into training. OUTCOME MEASURES: (1) Facial affect, (2) vocal affect, (3) affect from videos, (4) emotional inference from context, and (5) emotional behavior. There were 2 pretests, a posttest, and a 2-week follow-up. RESULTS: FAR participants showed significantly improved emotion recognition from faces, ability to infer emotions from context, and socioemotional behavior, while the SEI group members exhibited significantly improved ability to infer how they would feel in a given context. CONCLUSION: Training can improve emotion perception in persons with ABI. Although further research is needed, the interventions are clinically practical and show promise for the population with ABI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".