The Caregiver Transportation Scale Measures the Impact of Driving Cessation on Caregivers
Bibliographic record
Abstract
Abstract Driving cessation can impact retiring drivers, but we often neglect to consider its effect on caregivers. Caregivers may have to deal with important changes when someone they care for ceases driving, but we have few means to quantify these changes. Hence, we aimed to develop the Caregiver Transportation Scale (CTS) to measure this impact. We developed a bank of positive and negative questions, then pre-tested it with a small sample of caregivers (N = 11), leading to reduction and refinement of the questions. We pilot-tested this set of questions with a larger caregiver sample (phase 2; N = 73). Preliminary validation of the tool relied on correlation analyses with the Zarit Burden Interview (ZBI) and relevant demographic questions. For phase 2, the mean caregiver age was 61.9 (SD = 10.23, range 20-83); most caregivers were female (80.8%) and were adult-child caregivers (61.7%). The final version of the CTS contains 24 items. Internal consistency (Cronbach’s alpha) was .90. The mean caregiver score was 64.75 (SD = 16.76, range 24-98); about 1/3 of caregivers’ scores fell above the middle possible score of 72. The scores were positively correlated with the Zarit Burden Interview (r = .74, p < .001) and negatively correlated with the availability of others to help with driving responsibilities (r = -.36, p = .002). The CTS has the potential to help inform, develop, and evaluate services for caregivers who provide transportation support for older adults who ceased driving. However, further validation is required before we recommend its use.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".