A scoping review of cardiovascular risk factor screening rates in general or family practice attendees living with severe mental ill-health
Bibliographic record
Abstract
BACKGROUND: Primary care is essential to address the unmet physical health needs of people with severe mental ill-health. Continued poor cardiovascular health demands improved screening and preventive care. No previous reviews have examined primary care cardiovascular screening rates for people living with severe mental ill-health; termed in the literature "severe mental illness". METHODS: A scoping review following Joanna Briggs Institute methodology was conducted. Cardiovascular risk factor screening rates in adults with severe mental ill-health were examined in general or family practices (as the main delivery sites of primary care). Literature published between 2001 and 2023 was searched using electronic databases including Medline, Embase, Web of Science, PsychINFO and CINAHL. Two reviewers independently screened titles and abstracts and conducted a full-text review. The term "severe mental illness" was applied as the term applied in the literature over the past decades. Study information, participant details and cardiovascular risk factor screening rates for people with 'severe mental illness' were extracted and synthesised. RESULTS: Thirteen studies were included. Nine studies were from the United Kingdom and one each from Canada, Spain, New Zealand and the Netherlands. The general and/or family practice cardiovascular disease screening rates varied considerably across studies, ranging from 0 % to 75 % for people grouped within the term "severe mental illness". Lipids and blood pressure were the most screened risk factors. CONCLUSIONS: Cardiovascular disease screening rates in primary care settings for adults living with severe mental ill-health varied considerably. Tailored and targeted cardiovascular risk screening will enable more comprehensive preventive care to improve heart health outcomes and address this urgent health inequity.
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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 | 0.020 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.026 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".