Diagnostic inequalities relating to physical healthcare among people with mental health conditions: a systematic review
Bibliographic record
Abstract
Background: Inaccurate diagnosis of physical health problems in people with mental health conditions may contribute to poorer health outcomes. We review the evidence on whether individuals with mental health conditions are at risk of diagnostic inequalities related to their physical health. Methods: We searched MEDLINE, PsycINFO, Embase, and CINAHL, 1 September 2002-18 Septemebr 2024 (PROSPERO 2022: CRD42022375892). Seventy-nine studies were eligible for inclusion. Risk of Bias (RoB) was assessed using the Newcastle-Ottawa or RoB2 tools and results were presented as a narrative synthesis. Findings: Findings from the included studies suggests that people with mental health conditions face diagnostic inequalities for their physical health. A minority of studies adopted a design that specifically measured professional- and service-related factors associated with diagnostic inequalities. Most studies, however, measured diagnostic endpoints only, meaning that no inference could be made regarding the relative impact of patients' and clinicians' behaviour in producing inequalities. Interpretation: Further investigations should consider the stage of the diagnostic process at which inequalities occur, to improve knowledge of the mechanisms underpinning diagnostic inequalities, and support the development of targeted improvement interventions. Funding: This study is funded by The Health Foundation's grant to the University of Cambridge for The Healthcare Improvement Studies (THIS) Institute. Grant number not applicable.
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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 | 0.011 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".