Exploring the Use of Social Network Analysis to Measure Social Integration Among Older Adults in Assisted Living
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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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".