Positive psychological well‐being in women with obesity: A scoping review of qualitative and quantitative primary research
Bibliographic record
Abstract
Abstract Background Positive psychological well‐being (PPWB) is generally associated with improved physical health, mental well‐being, and healthy behaviors. However, it is not clear how PPWB differs in women with obesity or if improving PPWB will improve their health. The objective of this study was to summarize the evidence on PPWB in women with obesity. Method A scoping review was conducted in APA PsycINFO, EMBASE, MEDLINE, Cochrane Central Register of Controlled Trials, CINAHL, SocINDEX, Family & Society Studies Worldwide, ProQuest Dissertations and Theses Global databases. Primary research studies, with an analysis of adult women with a BMI ≥30 kg/m 2 with measures of PPWB are included. Results Thirty‐two studies encompassing >57,000 women with obesity, measured constructs of PPWB included: self‐esteem, life satisfaction, positive affect, social support, vitality, happiness, self‐acceptance, and optimism. Most studies showed that PPWB was lower in women with obesity although this association dissipated in studies when health and negative social factors were considered. Improvements in PPWB were associated with weight loss and with successful lifestyle changes with and without weight loss. Positive psychological interventions (PPIs) were used to bolster psychological well‐being. PPIs were associated with improved measures of self‐esteem and well‐being. Conclusions Prospective longitudinal and intervention studies are required to understand how evaluating and fostering PPWB might support gender‐informed obesity care.
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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 | 0.059 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.024 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".