“It is not the diet; it is the mental part we need help with.” A multilevel analysis of psychological, emotional, and social well-being in obesity
Bibliographic record
Abstract
In this research, we explored the psychological, emotional, and social experiences of individuals living with obesity, and perceptions of health care providers. We conducted a theoretical thematic analysis using two theoretical frameworks applied to transcripts from a previous qualitative study. Themes from a mental well-being framework were subsequently categorized under five environmental levels of the Social-Ecological Model (SEM). Key mental well-being themes appeared across all levels of the SEM, except the policy level. For the individual environment, one main theme was food as a coping mechanism and source of emotional distress. In the interpersonal environment, two themes were (a) blame and shame by family members and friends because of their weight and (b) condemnation and lack of support from health professionals. In the organizational environment, one main theme was inadequate support for mental well-being issues in obesity management programmes. In the community environment, one major theme the negative mental well-being impact of the social stigma of obesity. An overarching theme of weight stigma and bias further shaped the predominant themes in each level of the SEM. Addressing weight stigma and bias, and promoting positive mental well-being are two important areas of focus for supportive management of individuals living with obesity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".