Informal Social Support Networks and the Maintenance of Voluntary Driving Cessation By Older Rural Women
Bibliographic record
Abstract
Seventy-five rural women over the age of 77 participated in this study to describe the impact of informal social support on the maintenance of voluntary driving cessation. After being screened for mental status, they completed a demographic questionnaire and the Lubben Social Network Scale (2006) Lubben, J. E. 2006. “Assessing social networks among elderly populations.”. In Handbook of geriatric assessment, 4th ed. Edited by: Gallo, J. J., Bogner, H. R., Fulmer, T. and Paveza, G. J. 245Sudbury, MA: Jones and Bartlett. [Google Scholar]. They also participated in a semistructured interview designed to probe factors leading to driving cessation and the ability to maintain it. Findings suggest that most participants stopped driving due to a decline in physical function and/or involvement in a nonfatal accident. Adequate support from family and friends was critical to the maintenance of driving cessation. Those with a limited informal social network resumed driving due to the lack of transportation, feelings of insecurity and fear for their survival, and the desire to assist friends who were less fortunate. Implications for community health nurses working in rural areas are discussed.
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".