Focus Group Findings for the Self-Report Safe Driving Behaviour Measure
Bibliographic record
Abstract
BACKGROUND: Older adults, family members, and professionals may benefit from a safe driving behaviour self-/proxy-report. PURPOSE: During development of the Safe Driving Behavior Measure (SDBM), we conducted focus groups to (1) generate items based on respondents' driving experiences, and (2) obtain SDBM item-refinement feedback. METHODS: Twenty-three older drivers (mean age 70.5, SD = 4.5) and eight family members (mean age 50, SD = 20) from Ontario and Florida described safe driving behaviour (Focus Groups 1 and 2) and critiqued the SDBM (Focus Group 3). We coded responses using content and thematic analyses. FINDINGS: Findings from Focus Groups 1 and 2 generated 23 themes (e.g., others' erratic driving) leading to 16 new items (e.g., avoiding collisions). Focus Group 3 findings generated 13 item revisions (e.g., indicating number of highway lanes). Implications. Using focus group findings, we created a version of the SDBM for future testing of construct validity with older drivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".