Coping Behaviors and Health Status during the COVID-19 Pandemic among Caregivers of Assisted Living Residents in Western Canada
Bibliographic record
Abstract
OBJECTIVES: To examine the prevalence of coping behaviors during the first 2 waves of the COVID-19 pandemic among caregivers of assisted living residents and variation in these behaviors by caregiver gender and mental health. DESIGN: Cross-sectional and longitudinal survey. SETTING AND PARTICIPANTS: Family/friend caregivers of assisted living residents in Alberta and British Columbia. METHODS: A web-based survey, conducted twice (October 28, 2020 to March 31, 2021 and July 12, 2021 to September 7, 2021) on the same cohort obtained data on caregiver sociodemographic characteristics, anxiety and depressive symptoms, and coping behaviors [seeking counselling, starting a psychotropic drug (sedative, anxiolytic, antidepressant), starting or increasing alcohol, tobacco and/or cannabis consumption] during pandemic waves 1 and 2. Descriptive analyses and multivariable (modified) Poisson regression models identified caregiver correlates of each coping behavior. RESULTS: Among the 673 caregivers surveyed at baseline, most were women (77%), White (90%) and age ≥55 years (81%). Alcohol (16.5%) and psychotropic drug (13.3%) use were the most prevalent coping behaviors reported during the initial wave, followed by smoking and/or cannabis use (8.0%), and counseling (7.4%). Among the longitudinal sample (n = 386), only alcohol use showed a significantly lower prevalence during the second wave (11.7% vs 15.1%, P = .02). During both waves, coping behaviors did not vary significantly by gender, however, psychotropic drug and substance use were significantly more prevalent among caregivers with baseline anxiety and depressive symptoms, including in models adjusted for confounders [eg, anxiety: adjusted risk ratio = 3.87 (95% CI 2.50-6.00] for psychotropic use, 1.87 (1.28-2.73) for alcohol use, 2.21 (1.26-3.88) for smoking/cannabis use). CONCLUSIONS AND IMPLICATIONS: Assisted living caregivers experiencing anxiety or depressive symptoms during the pandemic were more likely to engage in drug and substance use, potentially maladaptive responses. Public health and assisted living home initiatives that identify caregiver mental health needs and provide targeted support during crises are required to mitigate declines in their health.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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".