Pedestrian Anxiety Questionnaire for Psychological Assessments of Persons Injured in Traffic Accidents
Bibliographic record
Abstract
Background: Pedestrians injured by motorists and even some motorists injured in car accidents may subsequently develop anxiety when they are pedestrians in urban settings next to roadways with busy traffic.The present study introduces a questionnaire for assessment of such pedestrian anxiety and describes its validation.Method: A 23 item questionnaire was developed.Its first 13 items evaluate situational anxiety, i.e., the severity of anxiety associated with various urban situations when the pedestrian is near vehicular traffic.The next 4 items (Items 14 to 17) assess other related emotions and also physical reactions.The last 6 items (Items 18 to 23) assess the avoidance of proximity to busy vehicular traffic.Responses of 21 patients with post-accident pedestrian anxiety (8 men and 13 women, aged 15 to 79 years, with the average of 43.2 years, SD=18.1) were compared to responses of 33 normal controls (17 men and 16 women, ages 20 to 78 years, with the average of 49.0 years, SD=17.9). Results:The patients differed significantly from normal controls (Pearson r=.95) in their scores on the questionnaire, thus indicating good criterion validity.Convergent validity was indicated by its significant correlations with measures of post-accident symptoms in the whiplash spectrum (r=.73), insomnia (r=.72), post-concussive symptoms (r=.52), generalized anxiety (r=.50), depression (r=.38), and pain (r=.37). Discussion and Conclusions:The Pedestrian Anxiety Questionnaire is meant for use with patients in urban settings with lifestyles near busy roadways.The questionnaire is to provide a standardized assessment tool for behavior therapists.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".