Assessing Women Caregiving Role to People Living With HIV/AIDS in Nigeria, West Africa
Bibliographic record
Abstract
HIV/AIDS scourge remains high in most countries of sub-Saharan Africa such as Nigeria, which is home to about 3.3 million HIV positive individuals and represents the second largest burden of HIV/AIDS care, treatment and demand worldwide after South Africa. Anti-retroviral treatment options though a welcome development, has increased the number of people living with this chronic illness, and most of them depend on family members for physical and emotional support. Traditional gender norms in Nigeria ensure that legitimately, women and girls are the first options for caregiving roles. This mandatory role has in turn imposed psychosocial disruption in the lives of female family members in Calabar, Nigeria. This descriptive study utilized convenient sampling technique, Zarit Burden Interview scale and semistructured questionnaires for data collection (260 respondents), and data analyses were achieved using SPSS16.0. The study showed that a significant ( p < .05) proportion of women (91%) were involved in providing care, including children from 10 years and above. Caregivers had minimal social support which increased the burden they experienced. The need for policy that recognizes and supports female caregivers (“silent cornerstone”) to reduce burden and ensure high quality care of people living with HIV/AIDS (PLWHA) in Nigeria is advocated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".