Knowledge, Attitudes, and Practices Related to Uterotonic Drugs during Childbirth in Karnataka, India: A Qualitative Research Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: India has the highest annual number of maternal deaths of any country. As obstetric hemorrhage is the leading cause of maternal death in India, numerous efforts are under way to promote access to skilled attendance at birth and emergency obstetric care. Current initiatives also seek to increase access to active management of the third stage of labor for postpartum hemorrhage prevention, particularly through administration of an uterotonic after delivery. However, prior research suggests widespread inappropriate use of uterotonics at facilities and in communities-for example, without adequate monitoring or referral support for complications. This qualitative study aimed to document health providers' and community members' current knowledge, attitudes, and practices regarding uterotonic use during labor and delivery in India's Karnataka state. METHODS: 140 in-depth interviews were conducted from June to August 2011 in Bagalkot and Hassan districts with physicians, nurses, recently delivered women, mothers-in-law, traditional birth attendants (dais), unlicensed village doctors, and chemists (pharmacists). RESULTS: Many respondents reported use of uterotonics, particularly oxytocin, for labor augmentation in both facility-based and home-based deliveries. The study also identified contextual factors that promote inappropriate uterotonic use, including high value placed on pain during labor; perceived pressure to provide or receive uterotonics early in labor and delivery, perhaps leading to administration of uterotonics despite awareness of risks; and lack of consistent and correct knowledge regarding safe storage, dosing, and administration of oxytocin. CONCLUSIONS: These findings have significant implications for public health programs in a context of widespread and potentially increasing availability of uterotonics. Among other responses, efforts are needed to improve communication between community members and providers regarding uterotonic use during labor and delivery and to target training and other interventions to address identified gaps in knowledge and ensure that providers and pharmacists have up-to-date information regarding proper usage of uterotonic drugs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".