A Continuing Medical Education Initiative for Canadian Primary Care Physicians: The Driving and Dementia Toolkit: A Pre‐ and Postevaluation of Knowledge, Confidence Gained, and Satisfaction
Bibliographic record
Abstract
This study examined the effect of the Driving and Dementia Toolkit on physician knowledge and confidence gained and the anticipated change in patient assessment and evaluated the extent to which physicians found the material to be useful. Before receiving the driving toolkit, 301 randomly selected primary care physicians received a copy of the pretest questionnaire; 145 responded and met the eligibility criteria. This group was then sent the toolkit, a satisfaction a survey, and a posttest questionnaire. Physicians were faxed the questionnaires (with up to three reminders) and telephoned if necessary. Changes in pre- and posttest results were analyzed using the McNemar test and Wilcoxon signed rank test nonparametric procedures included in SPSS, Version 10.0, and paired-samples t test. Pre- and posttest data were available and could be matched for 86 physicians (59.3%) response. Knowledge and confidence increased significantly (P</=.05) for most of the toolkit content questions. There was also a clear intent on the part of study participants to begin including additional pertinent questions in the patient/caregivers interview when assessing a patient's fitness to drive. On a scale from 1 (low) to 10 (high), overall satisfaction with the toolkit rated an average of 8.4. Use of the toolkit resulted in a clear improvement in physicians' reported knowledge of and confidence in dealing with dementia and driving. Future applications of similar innovative continuing education models can be used for other areas such as disclosure of dementia diagnosis, capacity assessments, or end-of life issues.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".