Validation of James Whetstone's Measure of Amaxophobia
Bibliographic record
Abstract
Background: Whetstone's Vehicle Anxiety Questionnaire is a 31 item measure of driving anxiety (amaxophobia) as common in survivors of motor vehicle accidents (MVAs).Method: Scores on Whetstone's questionnaire were available for 53 survivors of car accidents (mean age 40.2 years, SD=13.6, 15 men, 38 women).Their scores on the Driving Anxiety Questionnaire and on Steiner's Automobile Anxiety Inventory were also available, as well as scores on measures of PTSD (PCL-5), and on scales of post-concussive and whiplash symptoms, pain, insomnia, depression, and anxiety.Whetstone's scores were also available for 24 normal persons (mean age 51.7 years, SD=17.5, 14 men, 10 women) who were never injured in a serious car accident.Results and Discussion: With respect to criterion validity, Whetstone's scores were significantly higher (r=.88) in the patients than in the controls.The highest Whetstone score of the controls was 19 and the lowest of the patients was 23: there was no overlap between the two groups.With respect to convergent validity, high correlations were found of Whetstone questionnaire to the Driving Anxiety Questionnaire (r=.83) and to the PCL-5 measure of PTSD symptoms (r=.78).Whetstone scores were found to be also highly correlated with the post-concussion syndrome (r=.63) and moderately with whiplash symptoms (r=.46), post-MVA insomnia (r=.56), ratings of post-MVA pain (rs ranging from .43 to .51), and ratings of depression (r=.40) and of generalized anxiety (r=.43).Significant correlation was also found of Whetstone to Steiner's Automobile Anxiety Inventory (r=.45). Conclusion:The results indicate an excellent criterion validity and convergent validity of Whetstone's Vehicle Anxiety Questionnaire as a clinical assessment tool for amaxophobia.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".