Perceived stress and burnout among volunteer caregivers working in <scp>AIDS</scp> care in <scp>S</scp>outh <scp>A</scp>frica
Bibliographic record
Abstract
AIMS: To conduct a quantitative investigation of stress and the relationship with burnout among AIDS care volunteers. BACKGROUND: Volunteer caregivers experience stress that could lead to burnout. Yet, very few studies quantify stress and its relationship with burnout among AIDS care volunteers. DESIGN: This study uses a cross-sectional, exploratory survey design. METHODS: Face-to-face interviews were conducted with 126 volunteer caregivers working in 13 semi-rural communities (townships) in Durban, South Africa in April 2009. All participants were women, Christian and with low levels of education. A 22-item instrument was drawn from Pearlin et al.'s role overload and role captivity scales, Van Dyk's stress factor scale and the Maslach Burnout Inventory. RESULTS: Most of the volunteers had moderate-to-high levels of stress. 'Role/work overload' 'lack of support' and 'overwhelming nature of the disease' explained most of the variance in stress. Volunteers' age and number of patients in their care were predictors of stress. Caring for only AIDS patients, lack of support, stress emanating from perceived stigma and lack of training; and the overwhelming nature of AIDS were predictors of burnout. CONCLUSION: High levels of stress could negatively impact volunteers' health and well-being and on-the-job performance. Policy makers must develop and fund home-based care models that take into account the stressors associated with AIDS care, by reducing the work load, providing ongoing psychosocial support and recruiting nurses to assist volunteers. The small non-probability sample used in this study highlights the need to treat the findings with caution.
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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 | 0.001 |
| 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".