A pilot study of a virtually delivered dissonance-based eating disorder prevention program for young women with type 1 diabetes: within-subject changes over 6-month follow-up
Bibliographic record
Abstract
Introduction: In an uncontrolled study, we previously demonstrated the feasibility and preliminary efficacy of our virtual diabetes-specific version (Diabetes Body Project) of the eating disorder (ED) prevention program the Body Project. The aim of the current study was to evaluate further this program for women with type 1 diabetes (T1D) by assessing within-subject changes in outcomes from pretest over 6-month follow-up. Methods: Young women with T1D aged 16–35 years were invited to participate in Diabetes Body Project groups. A total of 35 participants were allocated to five Diabetes Body Project groups (six meetings over 6 weeks). Primary outcome measures included ED risk factors and symptoms, and secondary outcomes included three T1D-specific constructs previously found to be associated with ED pathology: glycemic control as measured by HbA1c level, diabetes distress, and illness perceptions. Results: Within-subject reductions, with medium-to-large effect sizes, were observed for the primary (ED pathology, body dissatisfaction, thin-ideal internalization, and appearance ideals and pressures) and secondary outcomes (within-condition Cohen’s ds ranged from .34 to 1.70). Conclusion: The virtual Diabetes Body Project appears to be a promising intervention worthy of more rigorous evaluation. A randomized controlled trial with at least a 1-year follow-up is warranted to determine its efficacy compared to a control condition.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".