Perceived barriers to entry to the eating disorder specialty within the dietetics profession and differences by race & ethnicity: results of a cross-sectional survey.
Bibliographic record
Abstract
Specializing in treatment of eating disorders requires additional training for a Registered Dietitian (RD). Added barriers to specializing in eating disorder treatment may disproportionately affect dietitians from underrepresented race/ethnicity groups, discouraging entering the specialty. This cross-sectional study aimed to identify potential barriers for registered dietitians pursuing a specialty in eating disorder treatment, and compare barriers identified by RDs from underrepresented race/ethnicity groups in the profession to RDs who self-identify as white. A random sample of US dietitians were sent an electronic survey regarding their experience with entering this specialty. Common barriers identified through a review of literature were listed for participants to select and free response boxes were available to provide additional information. Free responses were coded and frequencies were calculated for both white participants and participants who identified as a person of color. Differences in the frequency of cited barriers between the two groups were analyzed using chi-squares. In our final sample (n=328) 11% of dietitians self-identified as coming from an under-represented demographic in the profession, while 88.7% self-identified as white. There was a significant difference in the number of white RDs who specialized in eating disorder treatment compared to underrepresented race/ethnicity RDs, (p < .048). The survey results suggest several barriers to the eating disorder specialty were experienced by all dietitians regardless of race/ethnicity. RDs from underrepresented racial/ethnic groups were more likely to report lack of race/ethnic representation as a barrier compared to white RDs (p < 0.022).
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 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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".