Exploring factors contributing to caregiver burden in family caregivers of congolese adults with suspected dementia
Bibliographic record
Abstract
INTRODUCTION: Predicting caregiver burden in individuals with suspected dementia - is critical due to the debilitating nature of these disorders and need for caregiver support. While some examination of the factors affecting burden has been undertaken in Sub-Saharan Africa, each country presents with its own unique challenges and obstacles. This pilot study investigates predictors of caregiver burden in family caregivers of individuals with suspected dementia living in the Democratic Republic of the Congo (DRC). METHODS: Linear and multiple regression analyses were conducted to explore factors associated with caregiver burden in 30 patient-caregiver dyads with the Zarit Burden Interview (ZBI) for caregiver burden evaluation. Cognitive impairments of patients were assessed using the Community Screening Instrument for Dementia, Alzheimer's Questionnaire (AQ), the African Neuropsychology Battery, and the Neuropsychiatric Symptoms Inventory (NPI). RESULTS: Average caregiver burden on the ZBI was 36.1 (SD = 14.6; range = 12-58). Greater impairments in patient cognition (orientation, visuospatial, memory, executive functioning), fragility, and neuropsychiatric symptoms (delirium, agitation, depression) were predictive of caregiver burden. After controlling for AQ scores and caregiver gender, greater symptoms of depression, and worse performances on verbal memory and problem solving were associated with greater caregiver burden. CONCLUSION: Worsening patient fragility, cognition, functioning, and neuropsychiatric symptoms influenced caregiver burden in caregivers of individuals with suspected cognitive impairment in the DRC. These findings are consistent with the prior literature. Future studies may wish to explore supportive factors and caregiver specific characteristics that buffer against perceived burden.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".