Measuring the impact of driving status: The Centre for Research on Safe Driving–Impact of Driving Status on Quality of Life (CRSD-IDSQoL) tool
Bibliographic record
Abstract
BACKGROUND.: Driving an automobile is often considered an activity of daily living and is crucial to quality of life for many individuals. Following driving cessation, quality of life may become compromised. PURPOSE.: The Centre for Research on Safe Driving-Impact of Driving Status on Quality of Life (CRSD-IDSQoL) was designed to measure various elements of quality of life and how those elements are affected by driving status. METHOD.: The CRSD-IDSQoL was cross-sectionally administered to a convenience sample of 114 individuals (mean age 65.8 years). Exploratory factor analysis was used to examine the factor structure. FINDINGS.: The results supported three factors. Following adjustments for conceptual fit, Cronbach's alphas for the Community Mobility, Emotional, and Resources and Safety domains were .82, .84, and .74, respectively. Community Mobility was positively associated with distance driven per week. IMPLICATIONS.: The CRSD-IDSQoL may be a useful tool to study quality-of-life impacts of driving cessation. Further evaluation of the tool is warranted.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".