Assessing Social Isolation: Pilot Testing Different Methods
Bibliographic record
Abstract
Social isolation is a significant public health problem among many older adults; however, most of the empirical knowledge about isolation derives from community-based samples. There has been less attention given to isolation in senior housing communities. The objectives of this pilot study were to test two methods to identify socially isolated residents in low-income senior housing and compare findings about the extent of isolation from these two methods. The first method, self-report by residents, included 47 out of 135 residents who completed in-person interviews. To determine self-report isolation, residents completed the Lubben Social Network Scale 6 (LSNS-6). The second method involved a staff member who reported the extent of isolation on all 135 residents via an online survey. Results indicated that 26% of residents who were interviewed were deemed socially isolated by the LSNS-6. Staff members rated 12% of residents as having some or a lot of isolation. In comparing the two methods, staff members rated 2% of interviewed residents as having a lot of isolation. The combination of self-report and staff report could be more informative than just self-report alone, particularly when participation rates are low. However, researchers should be aware of the potential discrepancy between these two methods.
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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 | 0.069 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".