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Record W7015604220

Study of driving cessation and subjective well-being : predictors, moderators, and comprehensive measures of well-being / by Garrett Kafka.

2017· dissertation· en· W7015604220 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2017
Typedissertation
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLocus of controlLogistic regressionExtraversion and introversionHuman factors and ergonomicsPoison controlInjury preventionBig Five personality traitsSet (abstract data type)
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.035
GPT teacher head0.319
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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