Examining the Cultural Competence of Third- and Fourth-Year Nutrition Students: A Pilot Study
Bibliographic record
Abstract
The purpose of this study was to provide preliminary Canadian research assessing nutrition students' cultural competence and to identify areas for future education initiatives in dietetic education that could ultimately improve dietitians' cultural competence. A mixed-methods study was conducted using a 24-item questionnaire that was administered to students enrolled in third- and fourth-year undergraduate nutrition classes (n = 133). In total, 115 questionnaires were analyzed for quantitative data, and 109 were analyzed for qualitative data. The students scored an overall medium-high level of cultural competence. Out of the 5 areas examined (skills, attitudes, awareness, desires, knowledge), students' multicultural knowledge scores were the lowest. It was found that a lower number of barriers to learning about other cultures were significantly associated with a higher overall cultural competence score, and taking a course in cultural foods significantly increased the students' knowledge and overall cultural competence (P ≤ 0.05). The qualitative data found that students felt the cultural competence curriculum had gaps and identified several ideas for improvement. In conclusion, this research data provides novel insights into the cultural competence of Canadian dietetic students and additionally supports future research and curriculum development to enhance cultural competence.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".