Acceptability of an Online Module Addressing Weight Bias: Perspectives and Attitudes of Undergraduate Health Students and Instructors
Bibliographic record
Abstract
Weight bias and discrimination are highly pervasive and harmful to Canadians with higher weights. Researchers and practitioners who deliver, evaluate, and advise on dietary and weight-related interventions may inadvertently perpetuate weight bias through their work; however, trainees in these fields rarely have access to weight bias education within their applied health programs. This study evaluated the acceptability of an online educational weight bias module developed for undergraduate students enrolled in health courses. The intervention included a pre-recorded 20-minute online module with prompts for reflection or discussion, a self-assessment quiz, as well as a separate module and range of resources for instructors. Overall, 211 students from applied health courses and 4 instructors completed an online survey querying the module's delivery, impact, and relevance. Students agreed that the module provided useful information (82%), was easy to understand (97%), and was the right length (75%), but reported wanting more interactivity and engagement with the content. Instructors found the module engaging and useful and expressed interest in additional resources and support for weight bias education. Future research should explore the impact of weight bias education on students' weight-related attitudes and perceptions as well as feasibility and relevance of online features such as multimedia tools.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".