Toward compassionate communication: a rapid review on facilitating the dementia disclosure process
Bibliographic record
Abstract
Individuals with dementia and their care partners often report dissatisfaction with the disclosure process due to insufficient information and support. Healthcare providers face challenges in effective communication and connecting patients to post-diagnostic resources. To address these issues and emphasize person-centered communication, especially in diverse communities, we conducted a rapid systematic review consolidating preferences for dementia diagnosis disclosure from patients, care partners, and healthcare providers. Searches across MEDLINE, PsycInfo, Emcare (January 2013-February 2023), and gray literature identified 21 relevant articles, including those focusing on Black and Chinese populations. Four themes emerged: (1) setting up appointments, (2) appointment content, (3) overall approach, and (4) cultural competence in dementia disclosure. The Setting, Perception, Invitation, Knowledge, Emotions, and Strategy/Summary (SPIKES)-D tool was noted but lacked real-world evaluations and cultural applicability. Our review highlights the need for tailored tools and standardized guidelines, particularly for culturally competent dementia disclosure practices. HIGHLIGHTS: More than 50% of individuals globally express dissatisfaction with dementia diagnosis disclosure. Primary care providers face significant challenges in communicating dementia diagnoses effectively. Adoption of person-centered approaches is critical in improving dementia disclosure practices.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".