Changes in Prevalence of Overweight and in Body Image among Fijian Women between 1989 and 1998<sup>**</sup>
Bibliographic record
Abstract
OBJECTIVE: To investigate changes in prevalence of overweight and obesity and in body image among ethnic Fijian women in Fiji during a period of rapid social change and the relationship between changes in body image and BMI. RESEARCH METHODS AND PROCEDURES: The study design was a multiwave cohort study of BMI in a traditional Fijian village over a 9.5-year period from 1989 to 1998. Cohorts were identified in 1989 (n=53) and in 1998 (n=50). Selection criteria included Fijian ethnicity, female gender, age of at least 18 years, and residence in a specific coastal Fijian village in 1989 and 1998, respectively. Assessments consisted of measurement of height and weight, collection of demographic data by written survey, and administration of the Nadroga Language Body Image Questionnaire. RESULTS: The prevalence of overweight and obesity was significantly different between the cohorts, increasing from 60% in 1989 to 84% in 1998 (p=0.014). In addition, the age-adjusted mean BMI was significantly higher in 1998 compared with 1989 (p=0.011). Finally, there were significant between-cohort differences in multiple measures of body image, which were mostly independent of BMI. DISCUSSION: At 84%, the prevalence of overweight and obesity in this community sample of Fijian women is among the highest in the world. The dramatically increased prevalence over the 9.5-year period studied corresponds with rapid social change in Fiji and significant shifts in prevailing traditional attitudes toward body shape.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".