Culturally sensitive CLEAR guidelines on disclosing and communicating a diagnosis of dementia
Bibliographic record
Abstract
Providing a dementia diagnosis is challenging, especially in primary care and considering diverse patient backgrounds. The Alzheimer Society of Canada (ASC), the College of Family Physicians of Canada, and the Canadian Consensus Conference on the Diagnosis and Treatment of Dementia (CCCDTD) guideline group partnered with patients, care partners, and clinicians to generate contemporaneous guidance for primary care practitioners. While relevant to all communities, Black and Chinese Canadians were formally represented in working groups. A literature review identified needs areas. Informed by Guidelines International Network (GIN) principles and through iterative group meetings with all partners, these needs were explored and incorporated into guidance. The Compassionate Language and Empathetic Approaches for Respectful Dementia Disclosure (CLEAR) offers recommendations on: holistic engagement, fostering hope, acknowledging care partners, identifying disclosing clinicians, appointment structure and environment, person-centered communication, specific discussion topics, and emotional supports, all through a cultural competence lens. These guidelines address communication challenges in disclosing a dementia diagnosis and enhancing care and support for persons living with dementia and their care partners. HIGHLIGHTS: Healthcare practitioners (HCPs) struggle with disclosing and communicating dementia diagnoses. Guidance is limited in primary care and different patient ethnocultural groups. We developed culturally sensitive guidelines with scripts and practical materials. Appropriate communication techniques and terminology are recommended in Compassionate Language and Empathetic Approaches for Respectful Dementia Disclosure (CLEAR). Patient-centered and holistic approaches for the patient and care partner are emphasized.
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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 | 0.060 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".