Weight Bias Among Nutrition and Dietetics Students in a Ghanaian Public University
Bibliographic record
Abstract
OBJECTIVES: To measure the internal consistency reliability of 3 weight bias scales among nutrition and dietetics students enrolled at a public university in Ghana and to use the Fat Phobia Scale (FPS) to determine the prevalence of weight bias and the differences in gender and body mass index. DESIGN: Online survey gathered self-reported height, weight, and demographic data. Explicit weight bias was assessed using validated FPS, Beliefs About Obese People, and Attitudes Toward Obese Persons scales. PARTICIPANTS: Sample of 172 students. MAIN OUTCOME MEASURES: Prevalence of weight bias. ANALYSIS: Cronbach α reliability test was used to measure the internal consistency of scales. The prevalence of weight bias was expressed as a percentage. Independent t tests and analysis of variance were used to explore differences in gender and weight categories. RESULTS: The reliability scores for FPS, Beliefs About Obese People, and Attitudes Toward Obese Persons scales were 0.92, 0.51, and 0.38, respectively. About 53% of participants expressed weight bias. A significant difference was observed for weight bias between overweight and obese participants, with participants with obesity showing greater weight bias (P = 0.03). CONCLUSION AND IMPLICATIONS: Fat Phobia Scale (most reliable) identified more than half of the students had a negative attitude toward obesity. Weight bias training within this population may improve attitudes toward obesity.
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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".