Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it