Measuring Driving-Related Attitudes Among Older Adults: Psychometric Evidence for the Decisional Balance Scale Across Time and Gender
Bibliographic record
Abstract
PURPOSE OF THE STUDY: The Decisional Balance Scale (DBS) was developed to assess older adults' attitudes related to driving and includes both intrapersonal and interpersonal motivations for driving. This study examined the psychometric properties of the DBS ratings across 3 time points in a sample of 928 older drivers who participated in the Canadian Driving Research Initiative for Vehicular Safety in the Elderly (Candrive). DESIGN AND METHODS: Measurement invariance of the DBS was assessed longitudinally and across gender. RESULTS: Confirmatory factor analyses revealed that a two-factor model (positive and negative attitudes) for both driving beliefs related to the self and other provided a good fit to the data at each time point. Measurement invariance was supported across time and gender. Significant associations between the DBS factor scores and other driving measures (e.g., perceived driving ability and self-regulatory driving practices) provided evidence of convergent validity. IMPLICATIONS: The DBS appears to be a robust instrument for measuring attitudes toward driving and is recommended for continued use in future research on driving behaviors with older adults.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".