Diversity and Equity in Dietetics and Undergraduate Nutrition Education in Manitoba
Bibliographic record
Abstract
PURPOSE: The objectives of this study were (i) to describe ethnicity, tuition funding sources, and living arrangements during degree among Registered Dietitian (RD) and non-RD alumni of the University of Manitoba's undergraduate nutrition program and (ii) to describe barriers to obtaining an internship among those who did not become an RD. METHODS: A 31-item, self-administered, online questionnaire was distributed to nutrition graduates. Binary logistic regression was used to test for predictors of RD status (vs. non-RD). RESULTS: Of the 195 participants who completed the survey (37% response rate), 68% identified as an RD and 31% did not. White students had 3.8 times higher odds of being an RD (P < 0.001) compared with students of an ethnic minority. Those who had received a student loan (P = 0.033) or lived with their parents during their degree (P = 0.004) also had significantly lower odds of being an RD. The most common barrier for not completing the dietetic internship by non-RDs was that the application process was too stressful. CONCLUSIONS: Results from this study highlight the need for the dietetics field to address systemic barriers for students of ethnic minorities and low socioeconomic backgrounds, including barriers during the degree program and in the internship selection process.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".