Improving Access to HIV Care for Young Mothers Living with HIV in Zimbabwe
Bibliographic record
Abstract
Background: HIV disproportionately impacts adolescent girls and young women living in Southern Africa. Rates of mother-to-child HIV transmission are also elevated in this group, highlighting the need for targeted interventions to improve the health of young mothers living with HIV. A non-profit organization in Zimbabwe, Zvandiri, founded a peer-led care model, the Young Mentor Mother (YMM) program, in response to this issue. Methods: Four University of Toronto students conducted semi-structured virtual interviews (N=29) among Zvandiri staff and YMMs to identify the benefits and challenges of the YMM program. We applied deductive and inductive thematic analyses to transcriptions and performed a qualitative analysis using Dedoose software. Results: Participant [AT1] narratives revealed several themes, including three vital benefits from the YMM program: 1) peer- support, 2) holistic care, and 3) women’s empowerment. Barriers to the success of the program identified from interviews mapped onto the social-ecological model, whereby structural factors (lack of funding, food insecurity), community-level factors (HIV-related stigma, socio-cultural differences in accepting HIV care), and interpersonal factors (intimate partner violence) were found to impact the implementation and scale-up of the program. Barriers to scaling-up the YMM program included limited funds, lack of resources, and cultural and geographic differences. We also identified socio-structural challenges to scale-up, such as intimate-partner violence, food insecurity, and HIV-related stigma. Conclusion: Zvandiri’s YMM program fills an important gap in HIV care for young women and girls and has several benefits, such as peer-support, holistic care, and women’s empowerment. Future research focused on the perspectives of clients, stakeholders, and young fathers will further inform the scaling of the program to new countries and regions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| 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".