Bolstering Access to HIV-Related Health care in Zimbabwe Among Young Mothers Living With HIV: Lessons Learned on HIV Health Promotion From Zvandiri’s Young Mentor Mother Program
Bibliographic record
Abstract
HIV disproportionately affects adolescent girls and young women living in Southern Africa. Rates of perinatal HIV transmission are high in this population, emphasizing the need for targeted health promotion and public health programming to improve the health of young mothers living with HIV. Zvandiri, a non-profit organization in Zimbabwe, created the Young Mentor Mother (YMM) program in response to this issue. This health promotion program uses peer-led service delivery conducted by trained young mothers living with HIV, called YMMs. We conducted semi-structured virtual interviews (N = 29) among Zvandiri staff and YMMs to identify benefits and challenges, and to inform future program scaling. We applied thematic analyses to the transcriptions. Participant narratives revealed several themes, including three key benefits from the YMM program: (1) peer support, (2) holistic care, and (3) women's empowerment. Participants also shared barriers to the success of the program, reflecting two overarching dimensions: (1) barriers related to scaling up the YMM program and (2) challenges related to addressing socio-structural factors. Barriers to scale-up included limited funds and resources, and food insecurity. Socio-structural challenges included HIV-related stigma, cultural and geographic differences, and intimate partner violence (IPV). These challenges align with the social-ecological model, whereby structural factors (lack of funding, food insecurity), community factors (HIV-related stigma, socio-cultural differences in accepting HIV care), and interpersonal factors (IPV) affect the implementation and scale-up of the program. We recommend future adopters of the YMM program to tailor the model for their community, prioritize peer supporter's well-being, foster women's empowerment, and adopt a holistic care approach.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".