Safe and Sound: Is Safeness a Specific Affective Dimension Related to Eating Disorder Behaviors?
Bibliographic record
Abstract
OBJECTIVE: Safeness is a warm, soothing emotional state that is often experienced in the presence of close others. Safeness is thought to be distinct from other positive emotions or the absence of negative emotions and is shown to predict mental health variables over and above other emotions. The current study investigated the unique role of safeness in relation to eating disorder symptoms. METHOD: Participants with eating disorders (n = 164) and those with no history of an eating disorder (n = 49) completed two weeks of ecological momentary assessment to measure affect and eating disorder symptoms. RESULTS: Safeness emerged as an emotional state distinct from negative and positive affect among women with eating disorders. Safeness predicted the presence (vs. absence) of an eating disorder. Safeness did not predict the occurrence of eating disorder behaviors in real-time when accounting for other emotions. The first binge-eating episode within a day resulted in improvements in safeness in the following hours, but did not result in improvements in other emotions. DISCUSSION: If replicated, our results suggest that safeness might be uniquely implicated in the maintenance of binge-eating episodes. These results highlight the potential importance of integrating interventions targeting safeness into eating disorder treatments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".