<i>Needles Don't Agree with Me, Pills Don't Agree with Me</i>: Experiences of Contraceptive Use among Pakhtun Women in Pakistan
Bibliographic record
Abstract
The sixth most populous country, Pakistan's modern contraceptive use rate is just 25%. Of the multiple reasons for avoiding contraceptives, women cite side effects as a significant deterrent to contraceptive uptake. Efforts to understand these side effects are limited by overreliance on the biomedical framework, which typically dismisses some of women's negative experiences and explanatory models as misperceptions. Drawing on 13 months of ethnographic data from a village in Khyber Pakhtunkhwa, Pakistan, our study sought to provide an emic description of contraceptive side effects. Respondents' described what we call "spiritual" and "somatic" side effects. While the latter included experiences such as irregular bleeding and leg pain, spiritual side effects had more severe implications ranging from job loss, birth defects, to child death. In a context of a firm belief that family planning was a sin, contraceptives were believed to negatively impact spiritual well-being and invite God's wrath. Our data suggest these perceptions and experiences played a crucial role in contraceptive decision-making. The spiritual and somatic experiences of contraceptive use described by respondents also demonstrate the importance of broadening dominant biomedical approaches to holistically understand contraceptive side effects and usage.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".