Non‐pharmacologicaL InterVEntions for Antipsychotic‐Induced Weight Gain (RESOLVE) in People Living With Severe Mental Illness: A Realist Synthesis
Bibliographic record
Abstract
INTRODUCTION: Antipsychotic medications are used to treat individuals with severe mental illness (SMI) but are associated with rapid weight gain and several physical and mental risk factors. Early, proactive weight management is necessary to preempt these risk factors. The aim of this research was to understand and explain how, why, for whom, and in what contexts non-pharmacological interventions can help to manage antipsychotic-induced weight gain. METHODS: A realist review was conducted to identify contextual factors and underlying mechanisms associated with effective, non-pharmacological weight management interventions for adults > 18-years old. Practitioners and lived experience stakeholders were integral. RESULTS: Seventy-four documents were used to construct a program theory and 12 testable context-mechanism-outcome configurations. People with SMI benefit from support when navigating interventions aimed at managing weight gain. From a practitioner perspective, a good therapeutic relationship is important in helping people with SMI navigate early diagnosis and treatment options and facilitate the exploriation of any pre-existing issues. Interventions that are flexible and tailored to the needs of individuals, ideally starting early in a person's recovery journey, are likely to yield better results. Additional sources of support include family, friends, and peers with lived experience who can help individuals transition to autonomous goal-setting. The review findings also emphasizes the significant effect of stigma/dual stigma on individuals with SMI and weight gain. CONCLUSIONS: Successful interventions are collaborative, flexible, and underpinned by early and comprehensive assessment with the use of appropriate behavior change approaches. The therapeutic relationship is key, with a destigmatizing approach required. A realist evaluation with primary data is currently underway.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".