Weight Inclusive Practice: Shifting the Focus from Weight to Social Justice
Bibliographic record
Abstract
Obesity is framed by mainstream media and health care professionals as an "epidemic" contributing to the ill health of the population. This paper reviews literature related to dominant discourses about weight in dietetics, drawing on literature from other health care disciplines, and how these discourses influence patient care. Emerging, competing discourses are also reviewed. Literature highlighted that dietitians and dietetic students are often biased and hold stigmatizing beliefs toward "overweight" and "obese" patients. No research has been conducted in Canada addressing this question, leaving this as an opportunity for future research. Weight stigma and interventions focused on weight have multiple negative implications for individuals, especially those living in larger bodies, including reluctance to seek health care, poor body image, subsequent weight gain, and increased disordered eating. There are alternative discourses emerging, which shift the focus away from weight and toward social justice. The ways in which dietetic students are trained to "manage" weight, and how dominant discourses influence this training, is an important area of future exploration. Dietetic professionals are encouraged to reflect on their weight biases and educate themselves on weight inclusive approaches to health, such as Health at Every Size and Well Now.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.049 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| 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".