Exploration of a new tool for assessing emotional inferencing after traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: To explore validity of an assessment tool under development-the Emotional Inferencing from Stories Test (EIST). This measure is being designed to assess the ability of people with traumatic brain injury (TBI) to make inferences about the emotional state of others solely from contextual cues. METHODS AND PROCEDURES: Study 1: 25 stories were presented to 40 healthy young adults. From this data, two versions of the EIST (EIST-1; EIST-2) were created. Study 2: Each version was administered to a group of participants with moderate-to-severe TBI-EIST 1 group: 77 participants; EIST-2 group: 126 participants. Participants also completed a facial affect recognition (DANVA2-AF) test. Participants with facial affect recognition impairment returned 2 weeks later and were re-administered both tests. MAIN OUTCOMES: Participants with TBI scored significantly lower than the healthy group mean for EIST-1, F(1,114) = 68.49, p < 0.001, and EIST-2, F(1,163) = 177.39, p < 0.001. EIST scores in the EIST-2 group were significantly lower than the EIST-1 group, t = 4.47, p < 0.001. DANVA2-AF scores significantly correlated with EIST scores, EIST-1: r = 0.50, p < 0.001; EIST-2: r = 0.31, p < 0.001. Test-re-test reliability scores for the EIST were adequate. CONCLUSIONS: Both versions of the EIST were found to be sensitive to deficits in emotional inferencing. After further development, the EIST may provide clinicians valuable information for intervention planning.
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.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".