Nurse practitioners' preferences for online learning regarding driving and dementia
Bibliographic record
Abstract
ABSTRACT: With a growing population of older adults living with dementia in the community, nurse practitioners (NPs) are increasingly expected to address issues of medical fitness to drive (MFTD) and driving cessation within their clinical practice. With their expertise in clinical assessment and communication skills, NPs are well suited to this area of practice. Studies that examined MFTD and/or driving cessation suggest that NPs want and need further knowledge and training with this population. As part of our aim to develop an online educational program on driving and dementia for health care providers, including NPs, this mixed-methods study explored NPs' preferences regarding the format and content for the proposed online program. Results from an online survey completed by 90 NPs and interviews with six NPs highlighted key areas of focus for virtual modules, where communication strategies, tools to assess MFTD, and the reporting process for medically unfit drivers were emphasized. Reflecting on their team approach to care, participants in this study preferred a hybrid approach of asynchronous and synchronous learning delivery for this educational program. The next step will be to evaluate this program and its impact on both NP knowledge and skills in terms of its real-world application.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".