Services, models of care, and interventions to improve access to cancer treatment for adults who are socially disadvantaged: A scoping review protocol
Bibliographic record
Abstract
Timely access to guideline-recommended cancer treatment is known to be an indicator of the quality and accessibility of a cancer care system. Yet people who are socially disadvantaged experience inequities in access to cancer treatment that have significant impacts on cancer outcomes and quality of life. Among people experiencing the intersecting impacts of poor access to the social determinants of health and personal identities typically marginalized from society ('social disadvantage'), there are significant barriers to accessing cancer, many of which compound one another, making cancer treatment extremely difficult to access. Although some research has focused on barriers to accessing cancer treatment among people who are socially disadvantaged, it is not entirely clear what, if anything, is being done to mitigate these barriers and improve access to care. Increasingly, there is a need to design cancer treatment services and models of care that are flexible, tailored to meet the needs of patients, and innovative in reaching out to socially disadvantaged groups. In this paper, we report the protocol for a planned scoping review which aims to answer the following question: What services, models of care, or interventions have been developed to improve access to or receipt of cancer treatment for adults who are socially disadvantaged? Based on the methodological framework of Arksey and O'Malley, this scoping review is planned in six iterative stages. A comprehensive search strategy will be developed by an academic librarian. OVID Medline, EMBASE, CINAHL (using EBSCOhost) and Scopus will be searched for peer-reviewed published literature; advanced searches in Google will be done to identify relevant online grey literature reports. Descriptive and thematic analysis methods will be used to analyze extracted data. Findings will provide a better understanding of the range and nature of strategies developed to mitigate barriers to accessing cancer treatment.
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 | 0.088 | 0.075 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.085 | 0.013 |
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".