Initiatives to increase breast and cervical cancer–related knowledge, screening, and health behaviours among Black women
Bibliographic record
Abstract
SETTING: In Canada, racialized and immigrant women are typically under-screened for breast and cervical cancer. Under-screening is linked to numerous barriers to access, including lack of awareness, fear of pain, the stigma of cancer, socio-cultural factors like language, and various socio-economic factors. To address these barriers, our team developed a series of initiatives to promote awareness of breast and cervical health among Black women. INTERVENTION: Building on the development of a breast cancer resource hub for Black women, and in partnership with relevant community organizations, we implemented a series of virtual educational and cancer screening events (two of each thus far). Both event series were targeted towards Black women and tailored to their needs. OUTCOMES: Each educational event attracted more than 450 attendees and had average attendance times > 1 h. Most (> 87%) survey respondents agreed that an event specifically for Black women helped them feel supported. The 2022 and 2023 screening events provided breast and/or cervical cancer screening for 46 and 48 women, respectively. In both years, most women (> 90% of question respondents) noted that they were (extremely) likely to go for a mammogram or Pap test when next due. IMPLICATIONS: Both event series provided targeted opportunities for Black women to learn about prevention, risk factors, resources, and screening related to women's cancers. It is possible that, over time, such culturally tailored events can reduce or remove the stigmas associated with cancer and decrease differences in cancer-related knowledge and behaviours between racialized and non-racialized groups.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".