Convergent Validity of Leon Steiner's Measure of Driving Phobia
Bibliographic record
Abstract
Background: There is a great need for psychological instruments to evaluate post-accident driving anxiety in a standardized manner.Steiner's Automobile Anxiety Inventory is a 23 item questionnaire of which 18 can be scored to provide a quantitative measure of vehicular anxiety (amaxophobia) as common in survivors of motor vehicle accidents (MVAs).Method: Scores on Steiner's questionnaire were available for 33 survivors of car accidents (mean age 39.5 years, SD=12.8, 9 men, 24 women).Their scores on the Driving Anxiety Questionnaire and on Whetstone Vehicle Anxiety Questionnaire were also available, as well as scores on measures of PTSD (PCL-5), and of post-concussive and whiplash symptoms, pain, insomnia, depression, and anxiety.Results and Discussion: Significant correlations of moderate size were found of Steiner's questionnaire to the Driving Anxiety Questionnaire (r=.49) and Whetstone questionnaire (r=.45) and also to the PCL-5 measure of PTSD symptoms (r=.57).Steiner's scores were significantly, but on a weaker level, correlated with scales of postconcussive and whiplash symptoms, but not with age, gender, measures of pain or insomnia, or with number of prior MVAs or with number of weeks since the MVA. Conclusion: The results indicate an acceptable convergent validity of Steiner's Automobile Anxiety Inventory asa brief screening tool for driving phobia in clinical settings.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".