Training Emotional Processing in Persons With Brain Injury
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.
Full frame distilled prediction
Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: ObservationalConsensus signal: none
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.944
- Threshold uncertainty score
- 0.416
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.319 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Journal of Head Trauma Rehabilitation
- Topic
- Traumatic Brain Injury Research
- Field
- Medicine
- Canadian institutions
- not available
- Funders
- not available
- Keywords
- PsychologyTraining (meteorology)Acquired brain injuryPhysical medicine and rehabilitationCognitive psychologyNeuroscienceMedicineRehabilitation
- Has abstract in OpenAlex
- yes