Perceptions and reported practices of pregnant women and mothers of children under two years of age regarding antibiotic use and resistance in Vientiane province, Lao PDR: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Understanding pregnant women and mothers' perceptions towards antibiotic use and resistance is essential for appropriate antibiotic use and limiting antibiotic resistance. This study aimed to explore perceptions and reported practices of pregnant women and mothers with children under two years of age regarding correct antibiotic use and antibiotic resistance in Vientiane Province, Lao PDR. METHODS: The study employed an exploratory qualitative research design using focus groups discussions (FGDs). Participants were purposively selected based on: being pregnant at third trimester and attending antenatal care and mothers with children under two years of age, attending the health facility for postpartum visit /vaccinations. Six focus group discussions were conducted in September 2019 with a total of 55 women. The FGDs were transcribed verbatim, data were analyzed first by coding then categorizing the data as we looked for patterns and themes by using the qualitative content analysis. RESULTS: Most participants had some understanding of antibiotics but wrongly believed antibiotics can be used to treat viral disease. Over half of the participants had heard the term "antibiotic resistance", but often believed it was their bodies, not the bacteria that developed antibiotic resistance. During pregnancy and for their infants, women preferred to use antibiotics only when prescribed by a doctor. Outside of pregnancy however, consuming antibiotics without a prescription was commonly reported. Participants wanted more information about the indications for antibiotic use and antibiotic resistance. CONCLUSIONS: More effort is required to increase the level of understanding, and practice of mothers to promote optimal antibiotic use. Mothers' desire to learn more, and their fundamental concern for their children, can be used to promote appropriate antibiotic use. Awareness raising should be complemented by efforts to address other determinants of inappropriate antibiotic use, including educating healthcare workers, and pharmacists and addressing health service determinants that contribute to inappropriate antibiotic use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".