Adolescents’ experiences with group antenatal care: Insights from a mixed‐methods study in Senegal
Bibliographic record
Abstract
OBJECTIVES: Group antenatal care (G-ANC) is an innovative model in which antenatal care is delivered to a group of 8-12 women of similar gestational age. Evidence from high-income countries suggests G-ANC is particularly effective for women from marginalised populations, including adolescents. The objective of this study was to examine the experiences of Senegalese adolescents engaged in group antenatal care. METHODS: This convergent parallel mixed-methods study is derived from a larger effectiveness-implementation hybrid pilot study conducted in Kaolack district, Senegal. Quantitative data for adolescent participants were collected through baseline and postnatal surveys and descriptively analysed. One-on-one interviews and focus-group discussions were conducted with adolescent participants, and qualitative data were analysed using qualitative descriptive analysis. RESULTS: Forty-five adolescents aged 15-19 participated in G-ANC, with a median age of 18 years. The majority (93.3%) were married, and 64.4% were nulliparous. Findings indicated similar levels of G-ANC participation for adolescent and adult women. The majority (93.1%) of participants who had previously attended individual ANC indicated they would prefer G-ANC to individual care for a future pregnancy. Qualitative findings indicated key facets of consideration relevant to G-ANC for adolescents include social connectedness, the influence of social norms and the opportunity for engagement in healthcare. CONCLUSIONS: This study suggests that G-ANC has the potential to be an adolescent-responsive and culturally appropriate method of delivering antenatal care in Senegal.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".