A LONGITUDINAL ANALYSIS OF CHANGE WITHIN THE CANDRIVE COHORT
Bibliographic record
Abstract
Most older adults will eventually have to stop driving.Readiness for this transition helps to reduce some of the adverse effects associated with driving cessation.To measure this readiness, Meuser et al. (2011) developed the Assessment of Readiness for Mobility Transition (ARMT) tool, and reported that it correlates with facets of mental health and personality, and self-reported physical function.Using data from the Candrive study, we sought to replicate those associations, and further explore correlations between the ARMT and cognition, additional measures of physical function, driving behaviour, and the Decisional Balance Plus scales (DBP; designed to measure stage of change within the context of driving restriction).A sub-sample of the Candrive cohort was recruited from 4 Canadian sites (N=205; mean age=78.81,SD=4.53).While associations did mirror those reported by Meuser et al., the magnitudes of our correlations were lower (e.g., ARMT total and the Geriatric Depression Scale =.368, p<.005 [Meuser] vs. .196,p=.005).The ARMT was not statistically significantly correlated with any cognitive measures (e.g., MMSE, MOCA, Trails A&B), number of medical conditions/medications, activities of daily living, or measures of driving restriction, avoidance, or frequency.Correlations between ARMT outcomes and the DPB Positive scales were all statistically significant.The results of the current study lend support to the reliability of the ARMT (despite weaker correlations), and shed light on other associations.The next phase of this research will involve assessing the validity of the ARMT as a tool to detect change by examining outcomes over time, and in relation to correlates.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".