What stops us from eating: a qualitative investigation of dietary barriers during pregnancy in Punjab, Pakistan
Bibliographic record
Abstract
OBJECTIVE: Adequate dietary intake during pregnancy is vital for the health and nutritional status of both mother and fetus. The nutritional status of reproductive age women in Pakistan is poor, with 14 % being underweight (BMI < 18·5) and 42 % experiencing Fe deficiency anaemia. This may stem from beliefs, practices and other barriers influencing dietary intake. This qualitative study seeks to determine which factors impact dietary intake during pregnancy in rural Punjab. DESIGN: In-depth interviews and focus group discussions were conducted and then analysed using thematic analysis. SETTING: Three purposively selected rural districts (Sahiwal, Okara and Pakpatan) with the highest prevalence of maternal and child malnutrition in the province of Punjab, Pakistan. PARTICIPANTS: Mothers with children under age two (n 29) and healthcare providers with at least 5 years of experience working in the district (n 12). RESULTS: We identified a combination of physiological, socio-cultural and structural barriers that inhibited healthful dietary intake during pregnancy. The primary physiological barriers to optimal dietary intake and dietary practices included food aversions and food cravings. Food classification, fear of a difficult childbirth, fear of high blood pressure and household food politics were the principal socio-cultural barriers. Additionally, two structural barriers, inadequate antenatal counseling and a lack of affordable food options, were identified. CONCLUSIONS: Our study demonstrates that complex barriers prevent pregnant women in the Punjab area from consuming adequate dietary intake and that antenatal health education programmes and structural interventions are needed to support healthful dietary practices during this critical period.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".