Unpacking Caregiver Distress: Key Predictors and Future Implications in International Community Mental Health Settings
Bibliographic record
Abstract
BACKGROUND: Studies have highlighted that caregivers of loved ones are experiencing distress, however, caregivers of those with mental health symptoms may experience additional distress due to the unpredictable nature of mental health conditions, as well as emotional and behavioural challenges, and lack of social support. APPROACH: As part of a larger study examining caregiver distress across home care, palliative care and community mental health populations, this presentation will focus on the contributing factors including client-level predictors, caregiver characteristics and service use on caregiver distress in the community mental health sector.The study sample involved community mental health clients in Canada (Ontario, Newfoundland, and New Brunswick), the United States (New York) and Switzerland assessed using the interRAI Community Mental Health (interRAI CMH) instrument between 2005 and 2023. The interRAI CMH was designed for community-based individuals and incorporates the needs, strengths, and preferences when assessing mental and physical health, social support, and service use.The main study outcome of interest was the presence of one or more indicators of caregiver distress: helper(s) unable to continue caring activities; primary informal helper expresses feelings of distress, anger, or depression; family or close friends report feeling overwhelmed by persons illness. Logistic regression analyses will be used to identify factors associated with caregiver distress. RESULTS: Caregiver distress was evident among 2% of caregivers for persons receiving community mental health services. 8% indicated that they were overwhelmed by the care recipient illness. Among primary caregivers experiencing distress, 36% were spouses/partners, 24% were parents or guardians and 9% were children of the care recipient. Furthermore, majority lived with the care recipient. Among those with distressed caregivers, the mean age of care recipients was 5, with 3% of care recipients between the ages of 8-24.Multivariate analysis identified age, depressive symptoms, aggressive behaviour, substance abuse problems, severity of self-harm, cognitive impairment, ADL impairment, social withdrawal and positive symptoms as significant client-level predictors. Significant caregiver characteristics included living with client, relationship to the client, and caregiver provides informal support for childcare, crisis support and ADL help. The service use variable nurse practitioner/doctor visits in the last 3 days was a significant predictor of caregiver distress. IMPLICATIONS: Caregiver distress affects approximately in 5 community mental health clients. This may lead to several adverse outcomes for the caregiver and client. The experience of distress is affected by client, caregiver and agency characteristics that are readily identified using the interRAI CMH. The present results point to implications on an individual, policy, and practice level. On an individual level, findings can aid in identifying factors that are amenable to interventions. On a policy level, findings can help better address the avoidable stress found among caregivers and improve the quality of care among care recipients. Lastly, on a practice level, findings demonstrate a demand for a caregiver self-report assessment such as the Self-report Carer Needs Assessment (SCaN) to understand the needs of caregivers supporting community mental health populations in order to provide necessary support unique to caregivers from this population.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".