Dietitians’ Participation in Anti-Racist Actions
Bibliographic record
Abstract
The year 2020 marked a “call to action” for racial equity/social justice. In addition to increased engagement in antiracist action by individuals racialized as Black, folks racialized as white increased their engagement though at rates neither equal to nor sustained as that of Black-identified individuals. “White Fragility,” the emotional responses of white-identifying individuals when asked to consider their role in racism, has been postulated as a reason why white-identifying individuals do not engage in antiracist actions. Similarly, the psychological constructs of emotional regulation and distress tolerance may explain white-identifying individuals’ reluctance to engage in antiracist actions as those constructs describe an individual’s ability to regulate/tolerate “negative” emotions. Using a cross-sectional survey, we conducted a pilot and feasibility study to evaluate white fragility, distress tolerance and emotional regulation as possible influences on white, non-Hispanic-identifying healthcare professionals’ lack of engagement in antiracist actions. We analyzed the responses of n=82 white, non-Hispanic credentialled dietetics professionals. Correlation analyses demonstrated a highly significant negative association between white fragility and engagement in antiracist actions (-0.55, p<0.001). Multiple linear regression analyses revealed white fragility as a highly statistically significant negative predictor of engagement in antiracist actions when relevant demographic factors are controlled for (β-0.43; CI: -3.52, -1.15; R2=.34). Emotional regulation and distress tolerance were not significant predictors of engagement in antiracist actions. Feasibility analyses showed inadequate response and completion rates to allow for adequate power for analyses. Larger studies to explore these relationships are warranted, and strategies to improve recruitment, response, and completion rates are essential.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".