Refresher Courses for Senior Drivers in Sweden
Bibliographic record
Abstract
Functional abilities used for driving decline as we age with a potential risk increase. Experience and careful behaviour can to some extent compensate for degraded abilities. However, functional degradation can develop slowly and in disguise. Furthermore, changes in traffic and vehicle design could increase the demands on the aging driver. Thus, there is a risk for both over and under confidence in driving performance. Refresher courses for senior drivers can be a means which has been used in some countries with good results to promote a sustained or even improved safe mobility (e.g. USA and Canada). A recent evaluation of such a refresher course conducted in Norway revealed a tentative reduction in risk for participants one year after completion. Similar courses are also organised in Sweden. However, Swedish courses have never been evaluated. Thus, as a first step a survey was carried out by means of structured telephone interviews among stakeholders and organiser of refresher courses. The survey provided valuable knowledge concerning, for example, course content, organisation, and execution. The courses were in general highly appreciated. However, there were concerns regarding recruitment of participants, lack of evaluation, and a demand for more practical training and even a strong interest in driving simulators. As a second step a survey was planned and conducted among past and present course attendants. Thus, 162 course participants of which 112 were past attendees and 50 had just started the course answered two separate questionnaires (pre and post course). According to the pre-questionnaire senior drivers are quite careful drivers and specifically female drivers. However, it was also found that being able to drive was considered very important and contributes to health and well-being. Thus, refresher courses can fill a need for senior drivers and improve safe mobility. The post-questionnaire results revealed participants as eager to learn and curious. Courses, leaders and material were highly rated and their expectations were well met. However, a lack of practical training was noted. Furthermore, a majority felt they were better and safer drivers after the course even if behaviour and driving frequency was not changed. Knowledge of about 6 out of 25 course topics was improved but in general not typically manifested in perceived driving behaviour changes. It was found that course participation contributed to positive feelings in general. However, answers gave a hint that deciding when to give up driving can be a difficult task. Finally, ideas are given for further work and for improved evaluation efforts by connecting the investigation to the Goals for Driver Education framework.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".