Dementia Diagnosis Disclosure
Bibliographic record
Abstract
This paper reports the findings of a descriptive, exploratory, qualitative study of patient and caregiver perspectives of the disclosure of a dementia diagnosis. Data were collected at 3 points in time: (1) the disclosure meeting, (2) patient and caregiver interviews, and (3) focus group interviews. Thirty patient-caregiver dyads participated in the disclosure meetings at the Geriatric Day Hospital at the Ottawa Hospital, Ottawa, Canada. Within a week of the disclosure of diagnosis, 27 (90%) patients and 29 (97%) caregivers were interviewed in their homes, and 12 caregivers participated in 3 focus group interviews within 1 month after the disclosure meeting. Most patients and caregivers said they preferred full disclosure of the diagnosis. Patients expressed satisfaction with the physician providing the diagnosis and with their caregivers being present at the disclosure meeting, however, wanted more information about their condition. Caregivers provided further insight regarding the patient response, and suggested the need to emphasize hope in the face of a difficult diagnosis, the use of progressive disclosure to allow the person (and caregivers) to prepare, and the provision of detail about the disease and its progression.
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.
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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".