Predictors and experiences of seeking abortion services from pharmacies in Nepal
Bibliographic record
Abstract
Abortion was legalized in Nepal in 2002; however, despite evidence of safety and quality provision of medical abortion (MA) pills by pharmacies in Nepal and elsewhere, it is still not legal for pharmacists to provide medication abortion in Nepal. However, pharmacies often do provide MA, but little is known about who seeks abortions from pharmacies and their experiences and outcomes. The purpose of this study is to understand the experiences of women seeking MA from a pharmacy, abortion complications experienced, and predictors for denial of MA. Data was collected from women seeking MA from four pharmacies in two districts of Nepal in 2021-2022. Data was collected at baseline (N = 153) and 6 weeks later (N = 138). Using descriptive results and multi-variable regression models, we explore differences between women who received and did not receive MA and predictors of denial of services. Most women requesting such pills received MA (78%), with those who were denied most commonly reporting denial due to the provider saying they were too far along. There were few socio-demographic differences between groups, with the exception of education and gestational age. Women reported receiving information on how to take pills and what to do about side effects. Just under half (45%) of women who took pills reported no adverse symptoms after taking them and only 13% sought care. Most women seeking MA from pharmacists in Nepal are receiving services, information, and having few post-abortion symptoms. This study expands the previous limited research on pharmacy provision of MA in Nepal using a unique dataset that recruits women at the time of abortion seeking and follows them over time, overcoming potential biases present in other study designs. This suggests that expansion of the law to allow pharmacy distribution would increase accessibility and reflect current practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".