Prevalence and correlates of anxiety and depression in caregivers to assisted living residents during COVID-19: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Family and friend caregivers play significant roles in advocating for and ensuring quality health and social care of residents in Assisted Living (AL) homes. However, little is known about how the COVID-19 pandemic and related visitor restrictions affected their health and mental well-being. We examined the prevalence and correlates of anxiety and depressive symptoms among caregivers of AL residents during the initial wave of COVID-19 in two Canadian provinces. METHODS: A cross-sectional web-based survey was conducted among family/friend caregivers of AL residents in Alberta and British Columbia (Oct 28, 2020-Mar 31, 2021) to collect data on their sociodemographic, health and caregiving characteristics, as well as concerns about residents' health and social care before and during the first wave of the pandemic. A clinically significant anxiety disorder and depressive symptoms were assessed with the GAD-7 and CES-D10 instruments, respectively. Separate multivariable (modified) Poisson regression models identified caregiver correlates of each mental health condition. RESULTS: Among the 673 caregivers completing the survey (81% for Alberta residents), most were women (77%), white (90%) and aged ≥ 55 years (81%). Clinically significant anxiety and depression were present in 28.6% and 38.8% of caregivers respectively. Both personal stressors (comorbidity level, income reduction, low social support) and caregiving stressors exacerbated by the pandemic were independently associated with caregiver anxiety and depression. The latter included increased concern about the care recipients' depression (adjusted risk ratio [adjRR] = 1.84, 95% confidence interval [CI] 1.19-2.85 for caregiver anxiety and adjRR = 1.75, 95% CI 1.26-2.44 for caregiver depressive symptoms) and reported intention to withdraw the resident from AL because of COVID-19 (adjRR = 1.24, 95%CI 0.95-1.63 for caregiver anxiety and adjRR = 1.37, 95%CI 1.13-1.67 for caregiver depressive symptoms). CONCLUSIONS: Caregivers of residents in AL homes reported significant personal and caregiving-related stressors during the initial wave of COVID-19 that were independently associated with an increased likelihood of experiencing clinically significant anxiety and depressive symptoms. Healthcare providers and AL staff should be aware of the prevalence and varied correlates of caregivers' mental health during public health crises so that appropriate screening and support may identified and implemented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".