Canadian Dietitians’ Understandings of, Attitudes Toward, and Engagement in Social Justice and Advocacy
Bibliographic record
Abstract
Purpose: To explore Canadian dietitians’ understandings of, attitudes towards, and engagement in socially just dietetic practice and advocacy. Methods: An online semi-qualitative survey sent to Canadian dietitians. Results: Respondents’ understanding of social justice and socially just dietetics practice highlights the unique ways that social injustice and health inequities may be addressed by dietitians. Overall, respondents had a positive attitude toward dietitians’ roles in social justice advocacy. Support for dietitians’ advocacy roles was highest for food-related concerns, but it declined for concerns with indirect connections to dietitians’ food and nutrition expertise (e.g., environment). However, respondents still had uncertainty and divided perspectives concerning if and how social justice fits within dietitians’ scope of practice. They also had concerns about barriers that they felt limit dietitians’ ability to perform socially just practice. Respondents shared efforts to engage in socially just practice at intrapersonal, interpersonal, and structural levels. Conclusions: Dietitians could be a critical influence on the determinants of health. This research points strongly to the need for a framework for socially just dietetic practice and advocacy that delineates what social justice looks like across the array of settings in which dietitians practice and within the array of fora that dietitians advocate to support nutrition and health.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".