Exploring the Use of Social Network Analysis to Measure Social Integration Among Older Adults in Assisted Living
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social integration is measured by a variety of social network indicators each with limitations in its ability to produce a complete picture of the variety and scope of interactions of older adults receiving long-term services and supports. The purpose of this study was to develop and evaluate the feasibility of collecting sociocentric (whole network) data among older adults in one assisted living neighborhood. The sociocentric approach is required to conduct social network analysis. Applying social network analysis is an innovative way to measure different facets of social integration among residents. Sociocentric data are presented for 12 residents. Network visualization or sociograms are used to illustrate the level of social integration among residents and between residents and staff. Measures of network centrality are reported to illustrate the number of personal connections and cohesion. The use of resident photographs helped residents with cognitive impairment to nominate individuals with whom they interacted. The sociocentric approach to data collection is feasible and allows researchers to measure levels and different aspects of social integration in assisted living environments. Residents with mild to moderate cognitive impairment were able to participate with the aid of resident and staff photographs. This approach is sensitive to capturing routine day-to-day interactions between residents and assisted living staff members that are often not reported in person-centered networks. This study contributes to the foundation for larger more representative studies of entire assisted living organizations that could in the future inform interventions aimed at improving social integration and cohesion among recipients of long-term services and supports.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it