Predictors of Loneliness and Transitions in Loneliness in Ontario Home Care Clients: Before and During the COVID-19 Pandemic
Bibliographic record
Abstract
Background \nOlder adults over the age of 65 receiving home care services are particularly vulnerable to experiencing loneliness and social isolation. Loneliness and social isolation have been associated with adverse health outcomes, including depression, cardiovascular disease, and mortality, as well as increased service utilization. Research has widely explored cross-sectional predictors of loneliness, though factors that predict the onset of loneliness, particularly in the home care population remain largely understudied. With the COVID-19 pandemic exacerbating rates of social isolation, loneliness, and exposure to predictors, further research is necessary to understand how the pandemic influenced the risk of loneliness and the onset of loneliness in the Ontario older adult home care population. \n \nObjectives \nThe goal of this research was to identify predictors of loneliness and the onset of loneliness that were significant prior to and during the first wave of the COVID-19 pandemic in Ontario. The way in which the COVID-19 pandemic modified the relationship between loneliness and predictors was also explored. \n \nMethods \nSecondary data analysis was conducted using Ontario interRAI Home Care data collected between September 1, 2018, to August 31, 2020. The sample was divided into two subsamples, the “comparison” and “COVID” sample to conduct respective bivariate and multivariate analyses. Bivariate analyses guided the development of six binary logistic regression models that were selected with modified stepwise selection. The final multivariate models determined cross-sectional predictors of loneliness at T1 and longitudinal predictors of the onset of loneliness at T2 in both sub-samples. Two additional models explored the main effect and interaction effects of the COVID-19 pandemic on the onset of loneliness across the entire study sample. A social isolation scale was developed to supplement the analysis. \n \nResults \nRisk of loneliness and onset of loneliness with found to be associated with several demographic, physical, clinical, psychological, social, and environmental variables. Variations in risk factor significance was present across models, though sex, LHIN region, sleep disturbance, ADL impairment, depressive symptoms and social isolation were consistent across all models indicating that these factors had a considerable association with loneliness prior to and during the pandemic. When significant, depressive symptoms, anhedonia, geographic variations, and social isolation demonstrated the strongest association with loneliness. The first wave of the COVID-19 pandemic led to a slight increase in loneliness rates and significant interactions demonstrated that the pandemic exacerbated the influence of several risk factors on loneliness. \n \nConclusion \nThe prevention and reduction of loneliness must be targeted through an integrated approach by practitioners, home care organizations, researchers, and program and policy makers to combat risk factors of all dimensions beyond those that are clinical. Future research should aim to fill the gaps presented in this research and work to develop evidence-based indicators and practice protocols to aid in systematic risk identification and intervention of loneliness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".