Psychometric Network Analysis of the Intuitive Eating Scale-2 in Chinese General Adults
Bibliographic record
Abstract
Intuitive Eating Scale-2 (IES-2) is a measurement of intuitive eating behaviors and has been validated, with traditional latent variable approaches, in youth and adults from a number of different populations, including college students in China. However, there still lacks the evaluation of the psychometric properties of the IES-2 in adults from the Chinese general population. Moreover, psychometric network analysis, as a complement to traditional latent variable approaches, has not been used for examining the psychometric properties of the IES-2. Thus, the present study aimed to use a psychometric network approach to evaluate the psychometric properties of the IES-2 in Chinese adults from the general population. A sample of 700 Chinese general adults (50% women; M age = 31.13 years, SD = 9.19) were included in the present study. Psychometric network analysis was performed by using EGAnet and psychonetrics packages on R 4.2.0. Exploratory graph analysis (EGA) identified four dimensions, which were well-separated in the estimated network. The network structure showed excellent stability and metric measurement invariance (i.e., network loadings) across men and women. Furthermore, several items in the IES-2 were identified as key nodes in the network of the IES-2 that may be important for the developement and maintenance of intuitive eating. For example, two items related to reliance on body cues were the most impactful nodes in the complete network. The findings of our study provide further understandings of the IES-2 from the perspective of network analysis and have implications for related applications of intuitive eating interventions for general populations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".