Depression in Ugandan caregivers of cancer patients: The role of coping strategies and social support
Bibliographic record
Abstract
BACKGROUND: Palliative care services involve the psychological care of the caregivers of cancer patients. Psychological conditions, especially depression among caregivers, distort caregiving roles; thus, it can increase a patient's psychological suffering. OBJECTIVE: To determine the prevalence of depression and associated coping strategies among caregivers of cancer patients at a rural cancer care facility. METHODS: This cross-sectional study was among 366 caregivers of cancer patients. The data was collected using a pretested questionnaire, where the symptoms of depression were assessed using the Patient Health Questionnaire-9 at a cutoff of 10 out of 27. The coping strategies were assessed based on the Brief-coping orientation to problems experienced Inventory. Logistic regression was used to determine the factors associated with depression. RESULTS: The mean age of the participants was 39.01 (±11.50) years; most were females (60.38%). The prevalence of depression was 8.2%. The identified factors associated with increased likelihood of depression were coping strategies: active coping (aOR = 1.55, 95% Confidence Interval (CI) = 1.05-2.28, p = 0.026), denial (aOR = 1.62, 95% CI = 1.20-2.19, p = 0.001), and humor (aOR = 1.43, 95% CI = 1.11-1.84, p = 0.005). However, coping with positive reframing reduced the likelihood of depression (aOR = 0.70, 95% CI = 0.52-0.94, p = 0.019). There was no significant association between depression and social support. CONCLUSION: The lower prevalence of depression reported in this study than in the prior Ugandan studies reflects that depression severity among caregivers in rural settings is less prevalent because of the fewer care-associated burdens they experience. Therefore, establishing palliative care near the patients can be a protective factor for caregivers' depression. In addition, the role of social support and coping strategies in depression might be helpful in mental health strategies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".