Can social network analysis help to include marginalised young women in structural support programmes in Botswana? A mixed methods study
Bibliographic record
Abstract
BACKGROUND: In Botswana, one fifth of the adult population is infected with HIV, with young women most at risk. Structural factors such as poverty, poor education, strong gender inequalities and gender violence render many young women unable to act on choices to protect themselves from HIV. A national trial is testing an intervention to assist young women to access government programs for returning to education, and improving livelihoods. Accessing marginalised young women (aged 16-29 and not in education, employment or training) through door-to-door recruitment has proved inefficient. We investigated social networks of young women to see if an approach based on an understanding of these networks could help with recruitment. METHODS: This mixed methods study used social network analysis to identify key young women in four communities (using in-degree centrality), and to describe the types of people that marginalised young women (n = 307) turn to for support (using descriptive statistics and then generalized linear mixed models to examine the support networks of sub-groups of participants). In discussion groups (n = 46 participants), the same young women helped explain results from the network analysis. We also tracked the recruitment method for each participant (door to door, peers, or key community informants). RESULTS: Although we were not able to identify characteristics of the most central young women in networks, we found that marginalised young women went most often to other women, usually in the same community, and with children, especially if they had children themselves. Rural women were better connected with each other than women in urban areas, though there were isolated young women in all communities. Peer recruitment contributed most in rural areas; door-to-door recruitment contributed most in urban areas. CONCLUSIONS: Since marginalised young women seek support from others like themselves, outreach programs could use networks of women to identify and engage those who most need help from government structural support programs. Methods that rely on social networks alone may be insufficient, and so a combination of approaches, including, for instance, peers, door-to-door recruitment, and key community informants, should be explored as a strategy for reaching marginalised young women for supportive interventions.
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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 | 0.040 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".