Exploring the impact of the COVID-19 pandemic on perceptual disturbances and dysfunctional eating attitudes and behaviors: A review of the literature
Bibliographic record
Abstract
From the outbreak of the novel coronavirus 2019 (COVID-19) a new physical and social distancing environment has changed our lives and, more particularly, the way of perceiving oneself, as well as eating attitudes and behaviors. An increasing number of studies have highlighted a risky scenario in terms of negative perceptions of one's body as well as disordered eating and eating disorder patterns in both clinical and general population. With regard to this postulate, this literature review posits two main concepts-perceptual disturbances and dysfunctional eating attitudes and behaviors-in the general and (sub-)clinical populations, to provide an understanding of these phenomena during the COVID-19 pandemic. The main objective of this article is to provide a comprehensive and critical review of published scientific literature about perceptual disturbances (i.e., negative body image, body image disturbances, low body esteem) and dysfunctional eating attitudes and behaviors, including disordered eating (e.g., restrictive eating, binge-eating episodes, overeating, emotional eating) and eating disorders features in community (i.e., general population) and clinical and sub-clinical samples worldwide during the COVID-19 pandemic. The PubMed, ScienceDirect, Ebsco, and Google Scholar databases were searched. The initial search produced 42 references. Scientific publications from March 2020 to April 2022 were included, and among the works compiled, only published research articles have been retained. Purely theoretical papers were also excluded. The final selection consisted of 21 studies, covering both community, clinical (i.e., eating disorder population), and sub-clinical samples. The details of the results are discussed taking into consideration the potential impact of changes in the way we perceive ourselves and interact with others (e.g., the popularity of videoconferencing and the over-use of social network sites due to social isolation) as well as changes in eating attitudes and behaviors, physical activity and exercise (e.g., as an emotional response to the insecurity generated by the pandemic context), in community and (sub-)clinical samples. The discussion sheds light on two outcomes: (1) a summary of findings with methodological considerations; (2) an intervention continuum to deal with the consequences of the COVID-19 pandemic; (3) and a final conclusion.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".