Interventions involving own treatment choice for people living with coexisting severe mental illness and type 1 or 2 diabetes: A scoping review
Bibliographic record
Abstract
AIM: The objective of this scoping review was to summarize, understand and provide an overview of the empirical literature on interventions involving own treatment choice for people with coexisting diabetes (type 1 and 2) and severe mental illness (SMI). METHODS: This scoping review undertook a systematic literature assessment. Searches were performed in MEDLINE, Embase, PsycINFO, Web of Science, CINAHL, the Cochrane Library and grey literature (OpenGrey, Google Scholar and Danish Health and Medicine Authority databases). Publications from 2000 to July 2020 were of interest. Studies were included if they involved the users' own choice of treatment. INCLUDED STUDIES: RCT, intervention, cohort and case-based studies. RESULTS: A total of 4320 articles were screened, of which nine were included. The review identified eight studies from the United States and one from Canada testing different interventions for people with SMI and diabetes (one diabetes education program, five randomized controlled trials, one retrospective cohort study, one naturalistic intervention program and one case vignette). The interventions described in the nine articles involved service users, the majority incorporated individualized healthcare plans, and all interventions were based on multidisciplinary teamwork. CONCLUSIONS: Research in the area is limited. Care management interventions tend to focus on a single condition, paradoxically excluding SMI during enrolment. Interventions aimed at people with both conditions often prioritize one condition treatment leading to an unbalanced 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 | 0.017 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".