Social inequalities in breast cancer screening: evaluating written communications with immigrant Haitian women in Montreal
Bibliographic record
Abstract
BACKGROUND: The province of Quebec (Canada) has implemented a breast cancer screening program to diagnose this cancer at an early stage. The strategy is to refer women 50 to 69 years old for a mammogram every two years by sending an invitation letter that acts as a prescription. Ninety per cent (90%) of deaths due to breast cancer occur in women aged 50 and over. Numerous studies have shown social inequalities in health for most diseases. With breast cancer, a significant paradox arises: its incidence is lower among disadvantaged women and yet, more of them die from this disease. The health care system might play a role in this inequality. The scientific literature documents the potential for creating such inequalities when prevention does not consider equity among social groups. Immigrant women are often disadvantaged. They die of breast cancer more than non-immigrants. Studies attribute this to late-stage diagnosis due to poor adherence to mammography screening programs. PURPOSE OF THE STUDY: The main objective of our research is to assess how Haitian immigrant women in Montreal are reached by the Quebec Breast Cancer Screening Program, and specifically how they perceive the mammogram referral letter sent by the program. METHODS: The study uses a two-step qualitative method: i) In-depth interviews with influential community workers to identify the most relevant issues; ii) Focus groups with disadvantaged women from Montreal's Haitian community. RESULTS: A mammogram referral letter from the Breast Cancer Screening Program may be a barrier to compliance with mammography by underprivileged Haitian women in Montreal. This might be attributable to a low level of literacy, poor knowledge of the disease, and lack of financial resources. CONCLUSION: Barriers may be underestimated in underprivileged immigrant and non-immigrant communities. A preventive strategy must be adapted to different sub-groups and must also take into account lower literacy levels. To increase mammography uptake, it is crucial that the benefits of prevention be clearly identified and described in understandable terms. Finally, economic access to follow-up measures should be considered.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".