Predictors of mental well-being among family caregivers of adults with intellectual and developmental disabilities during COVID-19
Bibliographic record
Abstract
Background Internationally, stresses related to the COVID-19 pandemic negatively affected the mental health of family caregivers of adults with intellectual and developmental disabilities (IDDs). Aims This cross-sectional study investigated demographic, situational and psychological variables associated with mental wellbeing among family caregivers of adults with IDDs during the COVID-19 pandemic. Method Baseline data from 202 family caregivers participating in virtual courses to support caregiver mental well-being were collected from October 2020 to June 2022 via online survey. Mental well-being was assessed using total scores from the Warwick-Edinburgh Mental Wellbeing Scale. Demographic, situational and psychological contributors to mental well-being were identified using hierarchical regression analysis. Results Variables associated with lower levels of mental well-being were gender (women); age (<60 years old); lack of vaccine availability; loss of programming for their family member; social isolation; and low confidence in their ability to prepare for healthcare, support their family member's mental health, manage burnout and navigate healthcare and social systems. Connection with other families, confidence in managing burnout and building resilience and confidence in working effectively across health and social systems were significant predictors of mental well-being in the final regression model, which predicted 55.6% of variance in mental well-being ( P < 0.001). Conclusions Family caregivers need ways to foster social connections with other families, and support to properly utilise healthcare and social services during public health emergencies. Helping them attend to their needs as caregivers can promote their mental health and ultimately improve outcomes for their family members with disabilities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".