Study of driving cessation and subjective well-being : predictors, moderators, and comprehensive measures of well-being / by Garrett Kafka.
Bibliographic record
Abstract
This project examined four issues with respect to driving cessation. The issues were: \n1) group differences as a function of driving status, \n2) prediction of driving cessation, \n3) the utility of using comprehensive measure of subjective well-being to assess the impact of driving cessation, and \n4) the identification of variables that moderate the relationship between \ndriving cessation and subjective well-being. Non-institutionalized persons age 55 years and older were recruited from community, volunteer, and non-profit organizations in Winnipeg, Manitoba and Thunder Bay, Ontario. Two-hundred and twenty-three participants ranging in age from 55 to 91 years completed the study. Of these, 193 (86.9%) were drivers and 29 \n(13.1%) participants were non-drivers. Drivers were younger, in better health, and had higher income and education. Drivers also scored higher on extraversion and lower on neuroticism. Drivers also reported higher life control and life purpose and a more internal locus of control. Among current drivers, logistic regression analysis revealed that psychological variables make a significant unique contribution to predicting driving cessation. Higher life purpose and a more internal locus of control decrease the risk of driving cessation. With respect to the \nthird issue, a comprehensive measure of subjective well-being, the MUNSH, provided a more sensitive assessment of the impact of a set of variables associated with driving cessation on subjective well-being. With respect to moderating factors, income health, and psychological variables moderated the impact of cessation on subjective well-being. The findings of this study indicate that psychological variables are important in driving issues and that it is valuable to study positive aspects of driving cessation in future research. \nStudy group participants from Winnipeg, Manitoba and Thunder Bay, Northwestern Ontario.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".