REalist Synthesis Of non-pharmacologicaL interVEntions for antipsychotic-induced weight gain (RESOLVE) in people living with severe mental illness
Bibliographic record
Abstract
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 pre-empt these risk factors. The aim 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 Realist review 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 74 documents used to construct programme theory and 12 testable context-mechanism-outcome configurations. People with SMI benefit from support when navigating interventions aimed at managing the weight gain. From a practitioner perspective, a good therapeutic relationship is important in helping people with SMI navigate early diagnosis and treatment options and facilitating exploring 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 emphasise 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 use of appropriate behaviour change approaches. The therapeutic relationship is key with a de-stigmatising approach required. A realist evaluation with primary data is currently underway. Practitioner Points Individuals with severe mental illness on antipsychotic medications are at high risk for rapid weight gain and associated adverse mental and physical outcomes. Early comprehensive assessment by knowledgeable, respectful practitioners promotes therapeutic relationship development and identification of individuals’ specific risk factors and support, such as pre-existing disordered eating behaviours and the presence of family/carer and peer support. Case management or care coordination needs to be strengthened to ensure individuals’ access to consistent primary and secondary services, as well as community-based services.
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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 | 0.023 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".