Weight bias among students and employees in university settings: an exploratory study
Bibliographic record
Abstract
Abstract Background Weight bias and stigmatization are highly prevalent in modern society, especially in educational settings, such as universities. Despite extensive documentation of the adverse consequences on students’ daily functioning and psychological health, there is limited literature regarding factors associated with weight bias and its extent in Quebec universities. Objectives This exploratory study aims to assess the prevalence of weight bias and experiences of weight-related stigmatization, as well as to examine their associations with gender, psychological health problems, and status (students or employees) in a college environment in the province of Quebec. Methods Participants were recruited via their university emails. A total of 292 students and 129 university employees participated in an online survey distributed via the secure REDCap platform. The following data was collected: sociodemographic information, status (students or employees), body weight, experiences of stigma, and prejudice towards people living with a higher weight (Fat Phobia Scale; FPS). Results Approximately half of the respondents reported experiencing weight-related stigma (44.7%), and half indicated holding prejudice towards overweight people (51.1%), with a moderate rate of bias according to the FPS (3.25). Experience of weight-related stigma was found to be associated with gender (X 2 = 7.88, p = 0.019), and a higher prevalence of psychological health problems (X 2 = 9.41, p = 0.002), while having prejudice was associated with gender, with men scoring higher at the FPS (F = 7.64, p = 0.006), but not with the status (student or employee). The regression model identified significant effects of status [F(4, 347) = 2.856, p = 0.005] and the interaction between gender and status [F(4, 347) = -2.326, p = 0.021] on the FPS scores. Conclusions Various factors are associated with the experience of weight bias and stigmatization towards people with higher weight in the college population. Campaigns to prevent and reduce weight-related bias should be aimed specifically at staff members as well as students. Future research should examine weight bias internalization as a mediator between self-perceived weight and prejudice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".