Bibliographic record
Abstract
Background and objectives: A practical measure is needed for studying negative attributions after traumatic brain injury (TBI). This study explored a widely used measure, the Ambiguous Intentions Hostility Questionnaire (AIHQ), for the first time in the TBI population. Primary study aims were to determine if attribution differences could be identified between participants with and without TBI using the AIHQ and examine if traditional attribution associations with anger and aggression were supported. Method: Eighty-five adults with TBI and 86 healthy controls (HCs) were recruited from 2 rehabilitation hospitals. Study design was a cross-sectional survey. Outcomes assessed such as attributions (intent, hostility and blame), anger and aggressive responses to hypothetical scenarios were measured with the AIHQ. Additionally, trait aggression was assessed with the Buss-Perry Aggression Questionnaire (BPAQ). Results: Compared to HCs, participants with TBI had stronger attributions (p .001), anger (p = .021), and aggressive responses (p = .002) to AIHQ scenarios. Attributions were significantly correlated with anger and behavioural (aggressive) responses to AIHQ scenarios and the BPAQ. Conclusion: Participants with TBI judged others' behaviours more severely than HCs. More negative attributions were associated with stronger anger and more aggressive responses. Consistent with past negative attribution research that used a longer, more complex measure, the AIHQ may be a practical instrument for assessing negative attributions after TBI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.010 | 0.039 |
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; both teacher heads agree on what is shown here.
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".