FRAMEWORK FOR DEVELOPING A MOBILITY MANAGEMENT PROGRAM FOR AGING DRIVERS; A CONSENSUS STUDY
Bibliographic record
Abstract
Determining the most effective means to help older people maintain their mobility and social participation once they cease driving has garnered increased attention due to the known health and social consequences of losing one’s license in later life. Our aim was to convene an international expert consensus panel to establish a conceptual framework for a Mobility Management Program. Eighty-five experts from six countries were invited to participate of which fourteen scientists and eight occupational therapists contributed. Using a secure, web-based portal, participants’ comments were entered anonymously then reviewed and summarized by four independent researchers. These summaries were taken forward to the next round for further consideration. The study ended once the majority of participants (n=16/22) indicated an additional round would not improve the framework, which occurred after four rounds. The consensus group agreed upon two core goals of a Mobility Management Program: facilitating a person’s transition from driver to using alternative means of transport and guiding persons through associated lifestyle adjustments. This program is to ensure mental and physical well-being, social and community participation, road safety, and maintain meaningful engagements. The theoretical models relevant to the construction of the program are: Trans-theoretical Model of Behavior Change, Health Action Process Approach (self-efficacy), Transactional Model, and Client-Centered Goal Setting Approach. The consensus group agreed on a person-centered approach that respects individual attitudes towards driving retirement through four critical phases: consideration, acceptance, action, and autonomy. A successful transition is achieved when emerging needs are met and managed by the individual/caregiver involved.
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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 | 0.122 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".