Social Justice and Dietetic Education: Are We Preparing Practitioners to Lead?
Bibliographic record
Abstract
Purpose: To explore dietetic practitioners’ perceptions of their education and training in the knowledge, skills, and confidence to understand social justice issues and to engage in socially just dietetic practice and social justice advocacy. Methods: An online semi-qualitative survey sent to Canadian dietitians. Results: Most respondents (n = 264; 81.5%) felt that knowledge- and skill-based learning about social justice and social justice advocacy should be a part of dietetic education and training. Reasons given by respondents for the importance of social justice learning include: client-centred care and reflexive practice, effecting change to the social and structural determinants of health, preventing dietitian burnout, and relevance of the profession. Yet, over half of respondents either strongly disagreed or disagreed that they were adequately prepared with the knowledge (n = 186; 57.4%), skills (n = 195; 60.2%), or confidence (n = 196; 60.5%) to engage in advocacy related to social justice concerns. Some questioned the practicality of adding social justice learning via additional courses to already full programs, while others proposed infusing a social justice lens across dietetic education and practice areas. Conclusions: Dietetic education and training must do more to prepare dietitians to answer calls for dietitians to engage in social justice issues through practice and advocacy.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.019 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".