Navigating a strange and complex environment: experiences of Sudanese refugee women using a new nutrition resource
Bibliographic record
Abstract
Cynthia A Mannion, Shelley Raffin-Bouchal, Christena Jane HenshawFaculty of Nursing, University of Calgary, Calgary, Alberta, CanadaBackground: Refugees experience dietary changes as part of the daily challenges they face resettling in a new country. Sudanese women seek to care and feed their families, but face language barriers in the marketplace, limited access to familiar foods, and forced new food choices. This study aimed to understand the acceptability of a purse-sized nutrition resource, “The Market Guide”, which was developed to help recently immigrated Sudanese refugee women identify and purchase healthy foods and navigate grocery stores.Methods: Eight women participated in a focus group, four of whom were also observed during accompanied grocery store visits. Individual interviews were conducted with four health care workers at the resettlement center to gather perceptions about the suitability of The Market Guide. Focus groups and interviews were audiotaped and transcribed. Data from field notes and transcripts were analyzed using grounded theory for preliminary open codes, followed by selective and theoretical coding.Results: The Market Guide was of limited use to Sudanese women. Their response to this resource revealed the struggles of women acculturating during their first year in Calgary, Canada. We discovered the basic social process, “Navigating through a strange and complex environment: learning ways to feed your family.” Language, transportation, and an unfamiliar marketplace challenged women and prevented them from exercising their customary role of “knowing” which foods were “safe and good” for their families. The nutrition resource fell short of informing food choices and purchases, and we discovered that “learning to feed your family” is a relational process where trusted persons, family, and friends help navigate dietary acculturation.Conclusion: Emergent theory based on the basic social process may help health care professionals consider relational learning when planning health promotion and nutrition activities with Sudanese families.Keywords: nutrition resource, dietary acculturation, Sudanese refugees, grounded theory, health promotion
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".