Existing Trainings and Deficiencies in the Education of Dietitians about Eating Disorders: A Systematic Review
Bibliographic record
Abstract
Dietitians' lack of knowledge about eating disorders (EDs) can hinder access to nutritional care and present iatrogenic risks for people with EDs. This systematic review described existing trainings and deficiencies in the dietitians' education about EDs. It was carried out following the PRISMA guidelines. A total of 10 databases were systematically searched for quantitative and qualitative literature and 11 studies met the inclusion criteria and were included. The included studies outlined that the current formal education opportunities for dietitians about EDs are insufficient. Education about EDs was associated with better clinical skills and self-efficacy in the field of EDs. A lack of knowledge about EDs was identified as a cause of unwillingness to work with people with EDs. Specific knowledge relative to EDs underlined as deficient included gastro-intestinal disturbances, mental comorbidities and the treatments available. The development of formal education for dietitians about EDs addressing their gastro-intestinal and mental aspects is advised. If effective, it could improve the dietitians' educational opportunities about EDs and increase their willingness to care for this clientele.
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 | 0.030 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".