Validation of the Insomnia Severity Index on Patients Recovering from High Impact Car Accidents
Bibliographic record
Abstract
Background: The Insomnia Severity Index (ISI) is widely used in clinical assessments of insomnia in patients injured in high impact motor vehicle accidents (MVAs). This study examines the criterion and convergent validity of ISI on this clinical population. Method: De-identified archival data were available on 112 post-MVA patients (37 men, 75 women, mean age 38.8 years, SD=13.1). They completed the ISI as well as the Brief Pain Inventory, the Rivermead Post-concussion Symptoms Questionnaire, the Subjective Neuropsychological Symptoms Scale (SNPSS), Items 10 to 12 of the Whiplash Disability Questionnaire (ratings of depression, anger, and of anxiety), Whetstone Vehicle Anxiety Questionnaire, Driving Anxiety Questionnaire (DAQ), Steiner’s Automobile Anxiety Inventory, and some of them also completed the PTSD Checklist for DSM-5 (PCL-5). The ISI responses were also available from a community sample of 21 controls (10 men, 11 women, mean age of 39.2 years, SD=18.5). Results: The mean ISI total score of post-MVA patients (23.6, SD=13.1) was significantly higher than the one of the controls (6.0, SD=5.4) and significant between groups differences in the same direction were also observed on all 7 individual ISI items: the magnitude of these underlying relationships ranged from Pearson point biserial r of 0.68 to 87. The ISI total score also significantly correlated with ratings of post-MVA pain, depression, generalized anxiety, scores on measures of the post-concussion and whiplash syndrome, PTSD, and on Whetstone’s and DAQ measures of post-MVA driving anxiety. Discussion and Conclusions: The results show an excellent level of criterion and convergent validity of ISI for clinical assessments of insomnia in post-MVA patients.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".