Eating and Psychological Profiles of Women with Higher Depressive Symptoms Who Are Trying to Lose Weight
Bibliographic record
Abstract
The aim of this study was to determine whether women with higher depressive symptoms differed from women with lower depressive symptoms on early weight-loss, eating behaviors and psychological profiles. Among a sample of 45 overweight/obese women who had undertaken a self-initiated weight-loss attempt, two groups were formed based on scores from the Beck Depression Inventory (BDI-II), one with lower depressive symptoms (BDI-II < 10; n = 21) and one with higher depressive symptoms (BDI ≥ 10; n = 24). Even if some women in the higher depressive symptom group did not reach the clinical cut-off for depression (BDI = 14), this group tended to lose less weight in the first two months of their weight-loss attempt and to show a more disturbed eating and psychological profile compared to the group with lower depressive symptoms. In addition, among women with higher depressive symptoms, eating and psychological variables were systematically related to one another whereas these variables were not related among the other group. Results highlight the relevance of considering the presence of depressive symptoms as a marker of clinical severity among the overweight/obese population, and suggest that the BDI-II could be an interesting screening instrument to identify this particular subgroup.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".