Cancer risk factors and access to cancer screening for people experiencing homelessness
Bibliographic record
Abstract
Abstract Background People experiencing homelessness (PEH) are significantly more likely to develop and die from cancer than the housed population. Major factors influencing outcome are timely detection and treatment, which especially populations with complex needs lack access to. This scoping review outlines cancer risk factors among PEH and their facilitators and barriers to access cancer prevention. Methods Databases were searched in Embase, Global Index Medicus, PubMed, Scopus, and Web of Science in February 2023. OpenGrey and Google were searched for grey literature. Peer-reviewed and grey literature on cancer risk factors among PEH and factors facilitating or hampering access to cancer prevention services were eligible and synthesized narratively. Results We included 40 articles published between 1998 and 2023. Most eligible studies were conducted in the US (n = 34; 85%), indicating a paucity of data from other regions. More than one-third of the studies were published between 2018 and 2023, suggesting a recent increase in research interest. Access to cancer screening was associated with factors on the individual level (e.g., psychological and physical factors), interpersonal level (e.g., practical support), system level (e.g., continuous care), policy level (e.g., interventions to facilitate access). Most eligible studies reported high tobacco use among PEH. Overall, cancer risk factors among PEH have been less studied and require further research. Conclusions Mapping the evidence on cancer risk factors as well as barriers to accessing preventive services in this underserved population is a necessary step to create evidence-based public health recommendations: (a) Due to higher cancer risk factors among PEH, preventive strategies tailored to this population are necessary; (b) innovative strategies such as patient navigation may be viable to increase participation in screening programs; (c) investment in research within the European context is necessary. Key messages • Findings suggest that a system-wide approach is needed to address the increased risks and facilitate timely uptake of cancer screening to ensure sustainable use of cancer prevention services in PEH. • Evidence suggests policymakers need to be aware of inequity in access to cancer preventive services among PEH.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".