Validity of Steiner’s Automobile Anxiety Inventory
Bibliographic record
Abstract
Background: Steiner’s Automobile Anxiety Inventory (AAI) is a 23 item questionnaire which provides a quantitative measure of vehicular anxiety (amaxophobia), common in survivors of motor vehicle accidents (MVAs). The present study examines criterion and convergent validity of the AAI. Method: De-identified data from a sample of 50 patients (mean age=39.1, SD=12.1; 17 men, 33 women) injured in high impact MVAs included the scores on Steiner’s AAI, as well as the pain ratings on the Brief Pain Inventory (BPI), scores on the Insomnia Severity Index (ISI), the Rivermead Post-Concussion Symptoms Questionnaire, Subjective Neuropsychological Symptoms Scale (SNPSS), Whetstone Vehicle Anxiety Questionnaire, and on Driving Anxiety Questionnaire (DAQ). The patients’ scores were compared to de-identified AAI data of 22 normal controls (mean age=45.9, SD=21.3; 10 men, 12 women). Results: Mean score of the patients on Steiner’s AAI (mean=15.0, SD=2.5) was significantly higher than the one of normal controls (mean=3.2, SD=3.8) in a t-test (t=15.6, df=70, p<.001). The underlying correlation is very high (r=.88): this indicates an excellent criterion validity. Satisfactory convergent validity is suggested by significant correlations (p<.001) of Steiner’s AAI scores to the Whetstone Vehicle Anxiety Questionnaire (r=.58) and Driving Anxiety Questionnaire (r=.52). The AAI correlated at p<.001 with post-accident neuropsychological impairments as measured by Rivermead (r=.89) and SNPSS (r=.72). Internal consistency of the AAI is satisfactory (Cronbach alpha=.95). Discussion and Conclusion: The results indicate satisfactory criterion and convergent validity of the Automobile Anxiety Inventory.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".