Towards a definition of anti-oppressive dietetic practice in Canada
Bibliographic record
Abstract
Increasingly, dietitians have found ourselves working with racialized clients, communities, and colleagues across the health and food systems in Canada. We are often asked to treat the adverse health outcomes of Black, Indigenous, and racialized communities resulting from these oppressions at the individual level. However, it is the role of dietitians to engage in efforts to "reduce health inequities and protect human rights; promote fairness and equitable treatment" (College of Dietitians of Ontario, 2019). An anti-oppression approach is required for dietitians to understand how their power and privilege shape the dietitian-client relationship. The purpose of this commentary is to propose a shift from cultural competence or diversity and inclusion in dietetics to an explicit intention of anti-oppressive dietetic practice. We begin our exploration from the Canadian context. We draw from our background working in health equity in public health, and our experiences facilitating equity training using anti-oppression approaches with dietetic learners and other public health practitioners. In creating a working definition of anti-oppressive dietetic practice, we conducted a scan of anti-oppression statements by health and social services organizations in Ontario, Canada, and literature from critical dietetics. A literature search revealed anti-oppressive practice frameworks in nursing and social work. However, this language is lacking in mainstream dietetic practice, with anti-oppression only discussed within the literature on critical dietetics and social justice. We propose that "dietitians can engage in anti-oppressive practice by providing food and nutrition care/planning/service to clients while simultaneously seeking to transform health and social systems towards social justice."
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.048 | 0.063 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".