Exploring factors affecting adherence to multiple micronutrient supplementation during pregnancy in Cambodia: A qualitative analysis
Bibliographic record
Abstract
For decades, iron-folic acid (IFA) supplements have been provided to pregnant women in Cambodia through antenatal care (ANC) services. However, mounting evidence suggests that multiple micronutrient supplements (MMS) are superior to IFA supplements in achieving positive pregnancy outcomes. The possibility of transitioning from IFA supplements to MMS in government-run health centres is currently being assessed in Cambodia. A crucial component of this assessment involves identifying factors that can influence adherence to MMS, as low adherence can reduce supplement effectiveness. Consequently, this study aimed to explore the potential barriers and enablers to MMS adherence and identify the strengths and challenges of current ANC services. Data were collected through nine focus group discussions with pregnant women (n = 19), family members (n = 18) and midwives (n = 18) and three in-depth interviews with maternal and child health chiefs (n = 3) in Cambodia and analysed via content analysis. Factors found to influence MMS adherence included attitudes, perceptions and beliefs about MMS; knowledge related to supplementation; ANC counselling; family influence; physical health; access to ANC; supply of MMS; and supplementation norms. Noted strengths of ANC services were the quality of ANC materials, tailored patient education, midwife-patient relationships and flexibility of provided services. Primary challenges related to poor availability of ANC materials, inadequate midwife training, heavy workload, limited funding and suboptimal physical spaces for delivering ANC services. To effectively promote MMS adherence, strategies must involve pregnant women, family members and community leaders; seek to address knowledge gaps and misconceptions related to MMS; and enhance the availability and accessibility of ANC services.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".