Prevalence and determinants of HIV testing-seeking behaviors among women of reproductive age in Tanzania: analysis of the 2022 Demographic and health survey
Bibliographic record
Abstract
AIM: HIV remains one of the major epidemics and public health concerns within low and middle-income countries such as Tanzania. This study aimed to assess the prevalence and the factors associated with HIV testing-seeking behaviors among women of childbearing age in Tanzania. METHODS: This study used the 2022 Tanzania Demographic and Health Survey dataset. The study utilized individual recodes (IR) files where data was collected using the Women's Questionnaire to analyze factors influencing HIV testing behavior among women, Descriptive analysis, and bivariate and multivariate logistic regressions were performed and all the data were processed and analyzed using STATA version 17 at 95% CI and significance level P < 0.05. RESULTS: This study included 2531 women with 90.0% having ever tested for HIV while 7.0% had never tested for HIV. Not employed [AOR:0.35, CI (0.20-0.61)] has lower odds of HIV testing than All-year employed status. Rural residents have reduced odds of HIV testing [AOR:0.43, CI (0.21-0.88)] compared to women living in urban areas. Those able to ask their partner to use a condom are more likely to have been tested with increased odds [AOR: 3.52, CI (2.31-5.37)]. Participants with a history of genital discharge [AOR:4.30, CI (1.28-14.46)] and those who don't know their genital discharge history have [AOR: 0.20, CI (0.07-0.55)] are significant for HIV testing. Women who have heard about PrEP but are not uncertain about its approval [AOR: 36.07, CI (3.33-390.25)], respondents who have tested before with HIV testing kits [AOR:35.99, CI (4.00-324.13)] and women who are aware of HIV testing kids but never tested with them before [AOR: 2.80, CI (1.19-6.58)] are predictors of HIV testing seeking behaviors. CONCLUSION: The government and other concerned agencies should introduce mobile or community-based testing units and subsidize testing costs to reach economically disadvantaged or rural populations. Promote Open Communication on Sexual Health: Public health campaigns should encourage open discussions about sexual health within relationships, emphasizing condom negotiation and mutual health checks as preventive measures. Raise Awareness and Accessibility of HIV Prevention Tools: Expand education on PrEP and HIV self-test kits to improve familiarity and acceptance, which may empower individuals to proactively seek testing. Integrate Sexual Health Screening into Routine Healthcare: Health facilities should incorporate HIV testing when individuals present with symptoms like genital discharge to improve early detection and intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".