Perceived Injustice and Its Correlates after Mild Traumatic Brain Injury
Bibliographic record
Abstract
Perceived injustice is a belief that one has been treated unfairly and disrespectfully, and is suffering unnecessarily as a result of another person's actions. Perceived injustice predicts chronic disability after musculoskeletal injury but to our knowledge has not been empirically studied in people with mild traumatic brain injuries (mTBIs). We examined perceived injustice and its correlates in patients who were slow to recover from mTBI. Patients (n = 102) were recruited from four concussion clinics. The sample was on average 41.2 years old (standard deviation [SD] = 11.7; range = 21-64), 53.9% were women, and patients were evaluated 2-26 weeks post-injury (mean = 12.1, SD = 6.3). Patients completed measures assessing perceived injustice (Injustice Experience Questionnaire; IEQ), post-concussion symptoms, post-traumatic stress, depression, pain, disability, and neuropsychological performance validity. Patients frequently endorsed items such as "I just want to have my life back" (85.2%) and "people don't understand how severe my condition is" (89.1%), with 23.5% of the sample scoring in the clinically significant range on the IEQ (Total Score >30). Internal consistency was high (Cronbach's α = 0.91). Patients who failed performance validity testing (Cohen's d = 0.48) or were seeking/receiving compensation (d = 0.92) reported greater perceived injustice. Greater perceived injustice was associated with greater post-concussion symptoms (r = 0.48), traumatic stress (r = 0.69), depression (r = 0.60), bodily pain (r = 0.32), and negative expectations for recovery (r = 0.40; all p < 0.01). Given that perceived injustice is a belief system that can influence health behaviors, it might be a viable target for psychological treatment.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".