A gendered analysis of living with HIV/AIDS in the Eastern Region of Ghana
Bibliographic record
Abstract
BACKGROUND: A gender gap exists in knowledge regarding persons living with HIV/AIDS in Ghana. Women living with HIV/AIDS (WLHIV) greatly outnumber males living with HIV/AIDS (MLHIV) in Ghana and Sub-Saharan Africa generally. This necessitates more gender-nuanced evidence-based information on HIV/AIDS to guide individuals, healthcare workers, and other stakeholders in Ghana particularly. This paper undertook a gender-focused analysis of the experiences of WLHIV and MLHIV in a municipal area in Ghana which has been most impacted by HIV/AIDS. METHODS: In-depth interviews of 38 HIV-positive persons recruited using combined purposive and random sampling for one month, were tape recorded and analyzed using thematic content analysis. Participants were out-patients who were receiving routine care for co-morbidities at two specially equipped HIV/AIDS Voluntary Counseling and Testing Centers in the Lower Manya Krobo Municipality (LMKM), Eastern Region, Ghana. RESULTS: Our data yielded three major themes: characteristics of participants, health status and health seeking behavior, and challenges encountered living with HIV/AIDS. Except for feeling of sadness due to their HIV/AIDS-positive status, there were significant differences in the experiences of MLHIV, compared to WLHIV. WLHIV were more likely to be housing insecure, unemployed due mostly to stigmatization/self-stigmatization, less likely to have revealed their HIV-positive status to multiple family members, and had more profound challenges regarding their healthcare. Most MLHIV expected, demanded, and had support from their wives; WLHIV were mostly single-never married, divorced or widowed (mostly due to HIV/AIDS). The vast majority of WLHIV complained of near-abject poverty, including for most of them, lack of food for taking their anti-retroviral medicines and/or taking it on time. CONCLUSIONS: The experiences of the MLHIV and WLHIV with living and coping with the virus mostly differed. These experiences were unequivocally shaped by differential socio-cultural tenets and gendered nuances; WLHIV had more negative experiences. Public education on the extra burden of HIV/AIDS on WLHIV, more social support, and affirmative action in policy decisions in favor of WLHIV in the study district are needed to seek public sympathy and improve health outcomes and livelihoods of WLHIV particularly. Further studies using multiple sites to explore these differences are warranted.
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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 | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".