Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Dementia often goes undiagnosed. A workshop was developed to provide primary care clinicians with a structured clinical reasoning approach to dementia diagnosis and brain map tool to differentiate type of dementia. The purpose of this study was to examine the impact of this approach on self-perceived changes in knowledge, confidence, and ability to assess and manage memory problems and on self-reported application of learning to clinical practice. METHODS: Participants of 20 workshops (N=392) were invited to complete a reaction survey and of these, participants of 12 consecutive workshops (N=242) were invited to complete a 3-month follow-up survey to assess application of new learning to clinical practice and challenges experienced in doing so. RESULTS: In total, 355 reaction and 108 follow-up surveys were completed. Mean ratings of usefulness reflected that participants considered the clinical reasoning approach and brain map very useful to learning and knowledge transfer. At follow-up, the majority of respondents reported they were more confident (79%) and better able to assess (79%) persons with cognitive impairment and more confident (88%) and better able to manage (86%) persons with cognitive impairment. A number of practice changes and challenges were identified. CONCLUSIONS: These results add to a growing literature on strategies to improve dementia care with effective continuing medical education. A structured clinical reasoning approach to cognitive impairment is effective in improving confidence and ability to assess and manage patients with cognitive impairment, although participants continue to experience challenges in managing this complex condition.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".