Item Development and Validity Testing for a Self- and Proxy Report: The Safe Driving Behavior Measure
Bibliographic record
Abstract
OBJECTIVE: We report on item development and validity testing of a self-report older adult safe driving behaviors measure (SDBM). METHOD: On the basis of theoretical frameworks (Precede-Proceed Model of Health Promotion, Haddon's matrix, and Michon's model), existing driving measures, and previous research and guided by measurement theory, we developed items capturing safe driving behavior. Item development was further informed by focus groups. We established face validity using peer reviewers and content validity using expert raters. RESULTS: Peer review indicated acceptable face validity. Initial expert rater review yielded a scale content validity index (CVI) rating of 0.78, with 44 of 60 items rated > or = 0.75. Sixteen unacceptable items (< or = 0.5) required major revision or deletion. The next CVI scale average was 0.84, indicating acceptable content validity. CONCLUSION: The SDBM has relevance as a self-report to rate older drivers. Future pilot testing of the SDBM comparing results with on-road testing will define criterion validity.
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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 | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".