Food choices and practices during pregnancy of immigrant women with high-risk pregnancies in Canada: a pilot study
Bibliographic record
Abstract
BACKGROUND: Immigrant women may be regarded as a vulnerable population with respect to access and navigation of maternity care services. They may encounter difficulties when accessing culturally safe and appropriate maternity care, which may be further exacerbated by language difficulties and discriminatory practices or attitudes. The project aimed to understand ethnocultural food and health practices and how these intersect in a particular social context of cultural adaptation and adjustment in order to improve the care-giving capacities of health practitioners working in multicultural perinatal clinics. METHODS: This four-phase study employed a case study design allowing for multiple means of data collection and different units of analysis. Phase one consists of a scoping review of the literature. Phases two and three incorporate pictorial representations of food choices with semi-structured photo-elicited interviews. This study was undertaken at a Prenatal and Obstetric Clinic, in an urban Canadian city. In phase four, the research team will inform the development of culturally appropriate visual tools for health promotion. RESULTS: Five themes were identified: (a) Perceptions of Health, (b) Social Support (c) Antenatal Foods (d) Postnatal Foods and (e) Role of Health Education. These themes provide practitioners with an understanding of the cultural differences that affect women's dietary choices during pregnancy. The project identified building collaborations between practitioners and families of pregnant immigrant women to be of utmost importance in supporting healthy pregnancies, along with facilitating social support for pregnant and breastfeeding mothers. CONCLUSION: In a multicultural society that contemporary Canada is, it is challenging for health practitioners to understand various ethnocultural dietary norms and practices. Practitioners need to be aware of customary practices of the ethnocultural groups that they work with, while simultaneously recognizing the variation within-not everyone follows customary practices, individuals may pick and choose which customary guidelines they follow. What women choose to eat is also influenced by their own experiences, access to particular foods, socioeconomic status, family context, and so on. The pilot study demonstrated the efficacy of the employed research strategies and we subsequently acquired funding for a national study.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".