Body perceptions, occupations, eating attitudes, and behaviors emerged during the pandemic: An exploratory cluster analysis of eaters profiles
Bibliographic record
Abstract
Introduction: COVID-19 pandemic negatively impacted people's mental and physical health. Three areas have been significantly impacted, among others: eating-related behaviors, occupational balance, and exposure to self-image due to videoconferencing. This study aims to explore and document eaters profiles that were reported during the pandemic in the general Canadian population using a holistic perspective, including body perceptions, attitudes, and eating behaviors (i.e., body image, behaviors, attitudes, and motivations regarding food), and occupations (i.e., physical activity and cooking). Methods: This cross-sectional study was conducted from May to September 2020. Two hundred and seventy-three Canada's residents, French speaking of 18 years of age and older, participated in an online survey on behaviors, attitudes, and motivations regarding food and eating as well as body image and occupations during the COVID-19 pandemic. A hierarchical cluster analysis was used to determine the eaters profiles. One-way ANOVA and Chi-square test were conducted to differentiate occupational characteristics between eaters profiles. Results: Three distinctive profiles were found during the COVID-19 pandemic and could be placed on a continuum: the Congruent-driven eater is at the functional pole of the continuum, whereas the Incongruent-driven eater is at the dysfunctional pole of the eaters continuum. In the middle of the continuum, the Incongruent-perceptual eater is at a critical crossing point. Significant differences were reported between eaters profiles. Discussion: The empirical results based on an eaters continuum conceptualization highlight the importance of understanding how people perceive their body to assess and promote food well-being.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".