A <scp>mixed‐studies</scp> systematic review of the experiences of body image, disordered eating, and eating disorders during the <scp>COVID</scp> ‐19 pandemic
Bibliographic record
Abstract
OBJECTIVES: This systematic review assessed the influence of the COVID-19 pandemic and associated restrictions on body image, disordered eating (DE), and eating disorder outcomes. METHODS: After registration on PROSPERO, a search was conducted for papers published between December 1, 2019 and August 1, 2021, using the databases PsycINFO, PsycARTICLES, CINAHL Plus, AMED, MEDLINE, ERIC, EMBASE, Wiley, and ProQuest (dissertations and theses). RESULTS: Data from 75 qualitative, quantitative, and mixed-methods studies were synthesized using a convergent integrated approach and presented narratively within four themes: (1) disruptions due to the COVID-19 pandemic; (2) variability in the improvement or exacerbation of symptoms; (3) factors associated with body image and DE outcomes; (4) unique challenges for marginalized and underrepresented groups. Disruptions due to the pandemic included social and functional restrictions. Although most studies reported a worsening of concerns, some participants also reported symptom improvement or no change as a result of the pandemic. Factors associated with worse outcomes included psychological, individual, social, and eating disorder-related variables. Individuals identifying as LGBTQ+ reported unique concerns during COVID-19. DISCUSSION: There is large variability in individuals' responses to COVID-19 and limited research exploring the effect of the pandemic on body image, DE, and eating disorder outcomes using longitudinal and experimental study designs. In addition, further research is required to investigate the effect of the COVID-19 pandemic on body image and eating concerns among minoritized, racialized, underrepresented, or otherwise marginalized participants. Based on the findings of this review, we make recommendations for individuals, researchers, clinicians, and public health messaging. PUBLIC SIGNIFICANCE: This review of 75 studies highlights the widespread negative impacts that the COVID-19 pandemic and associated restrictions have had on body image and disordered eating outcomes. It also identifies considerable variations in both the improvement and exacerbation of said outcomes that individuals, researchers, clinicians, and other public health professionals should be mindful of if we are to ensure that vulnerable people get the tailored support they require.
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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 | 0.038 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".