A Mixed-Studies Systematic Review on the Impact of COVID-19 on Body Image, Disordered Eating, and Eating Disorders
Bibliographic record
Abstract
Objectives. This review assessed the impact of COVID-19 and restrictions related to the pandemic (e.g., social distancing and lockdown) on body image, disordered eating, and eating disorder outcomes. Method. After registration on PROSPERO, a systematic search was conducted for papers published between 1 December 2019 and 1 August 2021, using the databases PsycINFO, PsycARTICLES, CINAHL Plus, AMED, MEDLINE, ERIC, EMBASE, Wiley, and ProQuest. Results. A final sample of 74 reports, describing 75 studies, was included, and data from qualitative, quantitative, and mixed-methods studies were synthesized using a convergent integrated approach. Four themes were generated: (1) disruptions due to COVID-19; (2) variability in the improvement or exacerbation of symptoms; (3) risk and protective factors; and (4) unique challenges for marginalized and underrepresented groups. Findings across studies showed variation in individuals’ responses to, and experiences of, the current pandemic. Discussion. There is large variability in how individuals respond to COVID-19 and limited research exploring the effect of the pandemic on body image, disordered eating, and eating disorder outcomes using longitudinal and experimental study designs. Based on the findings of this review, we recommend that individuals reduce time spent on social media, maintain contact with family and friends, make time for self-care, and keep daily routines. Additionally, researchers should target more diverse participant samples and conduct longitudinal research on risk and protective factors of COVID-19 and long-term outcomes. Finally, clinicians should consider adopting flexible treatment practices, taking into account COVID-19 restrictions, patient preferences, and unique participant needs.
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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 | 0.024 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".