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Record W4406100061 · doi:10.3390/bs15010047

Bridging the Gap: Interventions to Increase Cancer Screening Adherence in Individuals with Mental Disorders—A Systematic Review

2025· review· en· W4406100061 on OpenAlexaboutno aff
Paolo Lombardo, Ilaria Mussetto, Valentina Baccolini, Enrico Rosa, Alessandra Sinopoli

Bibliographic record

VenueBehavioral Sciences · 2025
Typereview
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsBridging (networking)Psychological interventionMental healthMedicinePsychologyPsychiatryClinical psychologyComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.175
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.335
GPT teacher head0.583
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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