Developing risk factor profiles for anorexia and bulimia nervosa in young adults
Bibliographic record
Abstract
Recent research has shown that weight concerns begin at a very early age. Marchi and Cohen (1990) found that a significant number of young children had levels of eating disorder symptoms high enough to be of concern. They also found that children were at risk of showing parallel problems in later childhood and adolescence. As well it has been shown that an estimated 2% to 3% of post pubertal girls and women suffer from eating disorders, and an additional 5% to 10% may have "subclinical" eating disorders. In an attempt to develop a profile of those individuals at risk for developing an eating disorder (anorexia nervosa or bulimia nervosa), 625 male and female undergraduate students from the University of Manitoba were given a number of questionnaires relating to family environment, self-esteem, depression, sex-role identification, body shape and eating attitudes and behaviors. Logistic regression procedures were used with three eating disorder measures in an attempt to determine the most succinct model for predicting correlates of risk for developing an eating disorder. Results indicated university attending females were at greater risk for developing an eating disorder than university attending males, however, males were also susceptible. Variables significant to each of the three measures varied, as did the predictive power (chi-square value) of each model. Variables such as self-esteem were more directly related to eating disorder outcome than variables such as age. Body shape dissatisfaction was highly correlated with eating disorders, and had both a mediating and moderating effect depending on the outcome measure used.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".