Safe Abortion Services in Nepal: Initial Years of Availability and Utilization
Bibliographic record
Abstract
INTRODUCTION: Following the liberalization of the very strict Nepalese abortion law in 2002, the first services for safe induced abortion were introduced in 2004 at the nation's largest women's hospital. This paper examines the client profile, the context of demand for services, affordability and satisfaction with services. DATA AND METHODS: Data for the analysis came from a survey of women who presented themselves at the hospital for induced abortion services and subsequently received the services. RESULTS: Based on a survey of 672 clients, the median age was 26, and most women were married with an average of two living children. The majority reported being impregnated by the husband. Nearly three out of five gave their primary reason for termination as already having the number of children desired; another 42% cited finances. About two-thirds made the decision to abort jointly with the male partner. Most were satisfied with the services received and expenses incurred. About two-fifths reported having used a modern contraceptive method at the time the unwanted pregnancy occurred, while 22.6% reported practising either the safe-period or withdrawal methods. CONCLUSION: The clinic has provided affordable, quality abortion services to women in need. Findings also suggest that many areas need services strengthened, including the continued role of the family planning program in preventing unintended pregnancies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".