“I know you didn’t want to stay”: emergency department conversations about disposition for people living with dementia
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: When people living with dementia present to the emergency department (ED), the disposition decision-to admit them to the hospital or discharge them home-can be difficult for providers. However, little is known about current real-world practices in disposition conversations. RESEARCH DESIGN AND METHODS: This ethnographic study used direct observations of ED encounters with people living with dementia, their care partners, and ED providers at a Veteran Affairs facility in the Southeast United States. Follow-up interviews were conducted with patients and care partners. Interview guides and code book were informed by the Ottawa Decision Support Framework. Data were analyzed using the constant comparative method. RESULTS: Data were collected over 45 days, with 20 ED encounters, 18 follow-up interviews, and baseline surveys obtained. For the 20 Veteran participants living with dementia, all were male, mean age was 79.4, and 50% were Black or African American. Major themes included: (1) Disposition conversations had significant variation in depth and content, (2) patient and care partner participation varied with disposition, and (3) satisfaction was driven by alignment of disposition preferences. DISCUSSION AND IMPLICATIONS: Our study suggests that there are no consistent formats of disposition conversations for people living with dementia. Improving quality may be most needed when preferences are misaligned, and this should be identified early in the encounter.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".