Understanding the effects of reductionist biological views of eating disorder etiology on patient attitudes and behavior
Bibliographic record
Abstract
While eating disorders were historically considered to be a result of psychological or environmental causes, current evidence suggests that eating disorders are the product of complex gene-environment interactions wherein heritable vulnerabilities are activated by multiple exposures to environmental stimuli over the lifespan. Despite the fact that this integrated biopsychosocial etiological view of eating disorders is accepted among many professionals in the eating disorder field, evidence suggests that the general public and some clinicians are susceptible to dualist, or reductionist, views of psychopathology. Currently, little is known about (a) the prevalence of reductionist biological views of eating disorder etiology in those with eating disorders (this view attributes the cause of eating disorders to predominantly biological factors but does not acknowledge psychosocial factors as important contributors), (b) the effects of reductionist biological views on clinical outcomes, and (c) the most effective methods for modifying these views. In this article, we present the results of a preliminary investigation on the relationship between perceived causes of eating disorders and the attitudes and behaviors of those with eating disorders. We then go on to propose specific avenues for further research on uncovering the effects of reductionist biological views of eating disorder etiology.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".