Interventions impacting the accessibility of sexual reproductive health services for head porters in sub-Saharan Africa- A scoping review protocol
Bibliographic record
Abstract
Head porters working in markets in sub-Saharan Africa (SSA) are one of the world's most vulnerable and socioeconomically disadvantaged groups. They consist predominantly of uneducated women and girls seeking to escape poverty, early marriage, and other issues of domestic violence. Most female head porters are in their reproductive years and often lack access to sexual reproductive health services (SRHS) despite being at high risk for sexually transmitted infections (STIs), unplanned pregnancies, and gender-based violence. The low priority for women and girls' SRH in many SSA countries highlights the need to explore the factors influencing the accessibility of services for failure to do so restrains human development. An initial search of the literature was conducted and revealed no current scoping or systematic reviews on the accessibility to SRHS for female head porters in SSA. We outline a scoping review protocol, using the Joanna Briggs Institute methodology, to determine the interventions that influence the accessibility of SRHS for female head porters in SSA. The protocol is registered with Open Science Framework (https://osf.io/hjfkd). Findings will not only be valuable for female head porters but for all vulnerable female groups in SSA who experience high SRH risks and social disparities.
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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 | 0.072 | 0.082 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.063 | 0.007 |
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".