A Decade of Dementia Care Training: Learning Needs of Primary Care Clinicians
Bibliographic record
Abstract
INTRODUCTION: Limited knowledge of dementia among health professionals is a well-documented barrier to optimal care. This study examined the self-perceived challenges with dementia care and learning needs among primary care clinicians and assessed whether these were associated with years of practice and perceived preparedness for dementia care. METHODS: Participants were multi-disciplinary clinicians attending a 5-day team-based dementia education program and physicians attending a similar condensed continuing medical education workshop. Pre-education, they completed an online survey in which they rated (5-point scales): interest in learning about various dementia-related topics, perceived challenges with various dementia-related practice activities and preparedness for dementia care, provided additional dementia-related topics of interest, number of years in clinical practice, and discipline. RESULTS: Thirteen hundred surveys were completed across both education programs. Mean ratings of preparedness for dementia care across all respondents reflected that they felt somewhat prepared for dementia care. Challenge ratings varied from low to very challenging and mean ratings reflected a high level of interest in learning more about all of the dementia-related topics; significant differences between disciplines in these ratings were identified. In most cases, perceived challenges and learning needs were not correlated with number of years in clinical practice, but in some cases lower ratings of preparedness for dementia care were associated with higher ratings of the challenges of dementia care. DISCUSSION: Clinicians perceived that their formal education had not prepared them well for managing dementia and desired more knowledge in all topic areas, regardless of years in practice. Implications for education are discussed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".