Development and Initial Validation of the Transportation Support Scale
Bibliographic record
Abstract
IMPORTANCE: Driving cessation affects older drivers and, possibly, also care partners (most of whom tend to be women). Although tools exist to assess the effects on family and friends of providing informal care to someone who needs assistance, no tool is available to clinicians that specifically focuses on the effects of driving cessation. OBJECTIVE: To develop the Transportation Support Scale (TSS) to measure care partners' responses-both negative and positive-to driving cessation and assuming transportation responsibilities. DESIGN: We developed a list of 98 items to capture the impact on care partners of providing transportation to older adults who have stopped driving. In Phase 1, we pretested the items qualitatively with a small sample of care partners. In Phase 2, we reduced the number of items and examined several psychometric properties of the TSS with a larger sample. SETTING: Community. PARTICIPANTS: Two convenience samples of care partners who provide transportation (Phase 1, n = 11; Phase 2, n = 66). RESULTS: The initial pool of items was reduced from 98 to 22. The final TSS has an internal consistency of .88 (Cronbach's α). Thirty-five percent of care partners' scores fell above the middle possible score; these care partners were likely experiencing a high negative impact related to providing transportation after driving cessation. CONCLUSIONS AND RELEVANCE: The TSS demonstrated adequate preliminary psychometric properties. We need additional research to further evaluate the psychometric properties of the TSS (e.g., test-retest reliability). A fully validated TSS may be useful to clinicians and researchers. What This Article Adds: The TSS has the potential to help clarify the perspective of care partners as well as inform the development and evaluation of services for care partners who are providing transportation to former drivers.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".