Oral health care challenges in individuals with severe mental illness: a qualitative meta-synthesis
Bibliographic record
Abstract
Background: Individuals with severe mental illness (SMI) experience significantly higher rates of poor oral health, including dental caries, periodontal disease, and edentulism, compared to the general population. This meta-synthesis investigates the challenges faced by individuals with SMI in managing oral health and potential solutions. Methods: A comprehensive literature search (2010-2024) was conducted across PubMed, Web of Science, Scopus, and Google Scholar for any-language studies. The meta-synthesis involved systematic article selection, quality appraisal, and thematic data extraction/synthesis. Results: From 1,698 records, 101 full-text articles were reviewed; 11 met the inclusion criteria. Findings consistently demonstrate a high prevalence of poor oral health outcomes (caries, tooth loss, periodontal disease) among individuals with SMI, alongside significantly lower engagement in oral hygiene (e.g., toothbrushing) and dental care-seeking behaviours. Key barriers include financial constraints, dental anxiety, medication side effects (notably xerostomia), and low oral health awareness. Stigma and inadequate dental professional training in mental health further impede access. Proposed solutions emphasise integrating oral health education into psychiatric rehabilitation, enhancing communication between dental and mental health providers, and developing tailored support systems. Evidence suggests a bidirectional relationship between oral and mental health. Conclusion: This meta-synthesis confirms a stark oral health disparity for individuals with SMI, driven by suboptimal hygiene, medication effects, limited health literacy, and formidable access barriers compounded by financial hardship and stigma. Addressing this requires urgent, coordinated integration of mental and oral healthcare through co-located services, interdisciplinary collaboration, and tailored interventions. Future research must prioritise quantitative studies to elucidate causal pathways and long-term impacts, rigorously examining the roles of gender, geography, environment, and comorbidities. Bridging this divide is an essential public health imperative demanding systemic reform. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024516535, identifier PROSPERO [CRD42024516535].
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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 | 0.058 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.017 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".