Social Engagement is Associated with Location-based Digital Markers on a Dementia Care Unit
Bibliographic record
Abstract
OBJECTIVE: Social engagement is an important contributor to quality of life and the overall health of people with dementia. There is an opportunity to develop an objective measure of social engagement by capturing factors such as the number and duration of social contacts, time in social settings, and social network metrics. The aim of this study was to examine the longitudinal relationship between clinical assessment of social engagement and digital markers of social behavior and networks derived from a clinical real-time location system (RTLS). DESIGN: Prospective observational study. SETTING AND PARTICIPANTS: Thirty-seven patients on a short-stay specialized dementia unit for behavioral and psychological symptoms of dementia (60-day average length of stay). METHODS: Location data were collected using a wrist-worn clinical RTLS. Features measuring social contact, time in social spaces, and social network analyses were extracted from the location data for each morning and evening shift. The association over time between average weekly features and weekly Revised Index of Social Engagement (RISE) assessment scores was investigated using univariate panel models. RESULTS: There was high variability within and between participants in the RTLS-derived digital markers of social behavior. Seven digital markers of social engagement were statistically associated with weekly RISE scores over time, including time spent in the dining hall, time without co-patient contact, number of contacts longer than 5 minutes in duration, and social network PageRank. CONCLUSIONS AND IMPLICATIONS: Location data collected in residential care environments can provide insights into patterns of social engagement in people with dementia.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".