THE EXTREME FACE OF SOCIAL ISOLATION: A COHORT STUDY OF UNBEFRIENDED INDIVIDUALS IN LONG-TERM CARE
Bibliographic record
Abstract
Abstract Even though social isolation is a significant predictor of poor health and mortality in older adults, very little is known about social isolation in long-term care (LTC) settings. The aim of this study was to describe the prevalence, demographic characteristics, health outcomes, and disease diagnoses of residents without family contact in Alberta LTC homes. Using data collected between April 2008 and March 2018, we conducted a retrospective cohort study using the Resident Assessment Instrument, Minimum Data Set, (RAI-MDS 2.0) data from 34 LTC facilities in Alberta. We identified individuals who had no contact with family or friends. Using descriptive statistics and binary logistic regression, we compared the characteristics, disease diagnoses, and functional status of individuals who had no contact with family and individuals who did have contact with family. We identified a cohort of 25,330 individuals, of whom 945 had no contact with family or friends. Different from residents who had family, the cohort with no contact was younger (81.47 years, SD=11.79), and had a longer length of stay (2.71 years, SD=3.63). For residents who had contact with family, residents with no contact had a greater number of mental health diagnoses, including depression (OR: 1.21, [95% CI: 1.06-1.39]), bipolar disorder (OR: 1.80, [95% CI: 1.22-2.68]), and schizophrenia (OR: 3.9, [95% CI: 2.96-5.14]). Interpretation: Residents without family contact had a number of unique care concerns, including mental health issues and poor health outcomes. These findings have implications for the training of staff and LTC services available to these vulnerable residents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".