Cancer screening programmes for refugees in Canadian primary care: A realist review
Bibliographic record
Abstract
Hundreds of thousands of people are diagnosed with cancer in Canada every year; an effective way to diagnose and treat cancers earlier and to improve mortality is to carry out organised cancer screening programmes in primary care. Canada accepts approximately 25,000 refugees annually, and refugees often carry a higher risk for cancer. While Canadian physicians have committed to a vision of equitable and accessible primary care, encompassed in the vision of a Patient’s Medical Home, Canada also has a complex network of health systems, each of which have different ways to approach cervical, breast, and colorectal cancer screening. This dissertation asks: why don’t refugees in Canada participate in these cancer screening programmes? To answer this question, a realist review was undertaken to dissect the contexts, mechanisms, and outcome configurations (CMOCs) presented in the literature. A realist review comprises a literature search, construction of initial programme theory, extracting data from literature to test the initial theory, and iteratively searching, testing, and synthesising evidence. There were very few primary sources to consult regarding the specific topic of refugees and cancer screening in Canada. However, from this small body of work, it was possible to find a number of CMOCs from which to provide an explanation of why refugees do and don’t access primary care cancer screening programmes, and to point toward future research topics. Health system complexity, language and culture differences, and difficulties creating a strong relationship with a primary care provider cause a lack of understanding of the importance and immediacy of screening. However, there are models of care as demonstrated in refugee-friendly clinics in Canada that can mitigate some of the barriers refugees face to participating in cancer screening. It is clear that additional research is required to ensure that refugees in Canada are cared for appropriately and equitably through reducing cancer mortality.
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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".