“Because I don’t come from the culture:” Examining dietitian’s experience promoting healthy dietary behaviors among Hispanic Caribbean clients in New York City
Bibliographic record
Abstract
Dietitians are key in addressing existing diet-related health inequities, yet, research is lacking concerning how dietitians’ experiences working within culturally diverse communities. Addressing this gap, we interviewed fifteen dietitians working with Hispanic Caribbean (HC) communities in New York City, an understudied community with high incidence of diet-related conditions, to assess the best practices and experienced barriers when working with these communities. The best practices identified included building rapport, incorporating cultural values, and addressing food access. However, the interviews revealed important barriers that prevented successful implementation of these best practices. These included personal factors (language, food culture knowledge, food preferences), lack of culturally-relevant resources, cultural challenges that prevented engaging clients in participatory decision making, and institution-related constraints (time scarcity and funding). The findings underscore the need for improved training for dietitians to be better equipped to work with communities with different cultural and economic backgrounds from their own, for more culturally-relevant resources and funding to address underlying causes of diet-related health disparities in minority populations, and for increased diversity in the profession. Furthermore, our focus on Hispanic Caribbean communities expands existing research promoting cultural competence in the profession, presenting experiences working in with this large segment of the Hispanic community in the United States.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| 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".