Bridging the Gap: Interventions to Increase Cancer Screening Adherence in Individuals with Mental Disorders—A Systematic Review
Bibliographic record
Abstract
Patients with mental illnesses adhere to organized cancer screening programs less frequently than the general population. This systematic review aims to examine the literature to identify studies that evaluate interventions designed to increase cancer screening adherence in people with mental disorders. The review protocol was registered (CRD42024510431) and Pubmed and Scopus were searched up to January 2024. Breast, colorectal, or cervical cancer screening were considered. We adhered to the PROSPERO guidelines. Study quality was assessed. Overall, six articles were included: two RCT studies, two before–after studies, and two cohort studies. Four interventions were conducted in the USA, one in Canada, and one in Japan. Two studies evaluated all three cancer screening programs, two studies evaluated breast cancer screenings, and two studies evaluated colorectal cancer screenings. The proposed interventions included patient navigation, case management, and support from staff members along with educational modules, decision counselling sessions, and enhanced primary care. The most consistent improvements in screening adherence were observed in breast and colorectal cancer screenings compared to usual care, particularly through interventions like patient navigation (colorectal cancer: 47.1% vs. 11.8%, p < 0.001) and case management. Further evaluations of interventions and their costs are still needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".