The use of yoga and mindfulness within an eating disorders population: Results of a scoping review
Bibliographic record
Abstract
Introduction Eating disorders (ED) are characterized by perturbed eating habits or behaviors (APA, 2013). Even if treatments are available, they need to be more adapted to ED (Monthuy-Blanc, 2018). A complementary approach as yoga or mindfulness demonstrated positive effects with ED, such as an augmentation of mindfulness while eating (Rachel, Ivanka, Amanda, & Carlene, 2013), a better body satisfaction (Beccia, Dunlap, Hanes, Courneene, & Zwickey, 2018; Neumark-Sztainer, MacLehose, Watts, Pacanowski, & Eisenberg, 2018) and less preoccupation with food (Carei, Fyfe-Johnson, Breuner, & Brown, 2010). As the effects of yoga and mindfulness vary between the different ED and different uses, it is difficult to generalize the results obtained about the efficacy of yoga or mindfulness with ED. Objectives A scoping review is actually done to map the evidence about the use (length, intensity, frequency) of yoga and mindfulness among ED and their effects. Methods The realization of the scoping review is based on the Joanna Briggs Institute Methodological Framework(Peters, Godfrey, McInerney, Baldini Soares, Khalil, & Parker, 2017). Research will be done in the following databases: CINAHL, PsycInfo, PubMed/MEDLINE, Web of Science, EBM Reviews/Cochrane. Different types of papers are going to be included and a content analysis is going to be done among the extracted data. Results Preliminary results of the scoping review are going to be presented. Conclusions Among the different treatments used with ED, yoga and mindfulness have demonstrated positive effects. These approaches as part of integrative health are helpful to improve physical and mental health of individuals suffering from ED.
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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 | 0.020 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".