Exploring the Associations between Media and Instagram Interaction Patterns with Weight Bias among Undergraduate Nutrition Students in the Brazilian Nutritionists’ Health Study
Bibliographic record
Abstract
This study examined the association between media and Instagram interaction patterns with weight bias among undergraduate nutrition students in the Brazilian Nutritionists’ Health Study. We also explored the potential mediating role of students’ own body image perception in these relationships. A total of 406 students (78% women) participated in this cross-sectional analysis. Sociodemographic data, media influence, Instagram interaction patterns, body image perception, and weight bias were assessed using semi-structured questionnaires. Findings indicated that exposure to fitness content on Instagram (β = 0.17, p < 0.001) and the pursuit of an ideal athletic body (β = 0.12, p = 0.034) were associated with increased weight bias. In contrast, engagement with body diversity content (β = −0.23, p < 0.001) and perceived pressure from media to conform to appearance ideals (β = −0.24, p < 0.001) had a mitigating effect on weight bias. Notably, body image perception did not mediate these relationships (p > 0.05). In conclusion, this study revealed a link between media exposure and weight bias among undergraduate nutrition students, independent of their body image perception. Developing social media literacy programs that encourage students to critically evaluate media content is imperative to reduce weight bias. Additionally, a deeper examination of the media content that contributes to weight bias and the potential need for targeted regulatory measures is warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".