Age matters: differential impact of service quality on contraceptive uptake among post-abortion clients in Kenya
Bibliographic record
Abstract
This paper analyses the impact of high quality, user-friendly, comprehensive sliding-scale post-abortion services on clients' uptake of contraception in a Kenyan town. Data were drawn from detailed physician records in a private clinic that served 1080 post-abortion clients in 2006. All clients received confidential family planning counselling and were offered a complete range of contraceptives at no additional cost. One quarter of clients were below age 19. Prior to the abortion, no client aged 10-18 years reported having used contraception, as compared to 60% of clients aged 27-46 years. After the abortion and family planning counselling session, only 6% of clients aged 10-18 chose a method, as compared to 96% of clients aged 27-46, even though contraception was free, the provider strongly promoted family planning to everyone and all clients had just experienced an unwanted pregnancy. Significant predictors of contraceptive uptake post-abortion were: having a child, a previous termination, prior contraceptive use and being older than 21. These findings suggest that availability, affordability and youth-friendliness are not sufficient to overcome psycho-social barriers to contraceptive use for sexually-active young people in Kenya. To reduce unwanted pregnancies, more attention may be needed to developing youth-friendly communities that support responsible sexuality among adolescents.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".