Creating Health Equity in Cancer Screening (CHECS): Developing Strategies for Underscreened Populations through Community Engagement
Bibliographic record
Abstract
Background: The East and Northeast areas of Calgary, Alberta experience higher material deprivation and lower breast, cervical, and colorectal cancer screening rates. This project aimed to reduce inequities in cancer screening in these areas by engaging with community members and health workers to better understand reasons and motivations for screening for cancer. The results of this engagement informed the co-design of an outreach strategy aimed at increasing cancer screening awareness, and ultimately participation. Methods: Community members and health workers were recruited through Facebook and partner organizations and participated in virtual focus groups and interviews, respectively. Focus groups were provided in five different languages: Malayalam, Punjabi, Urdu, Tigrinya, and English. Qualitative analysis of the engagement results was completed using NVivo software and was coded independently by two researchers using an inductive approach. Results: Fourteen focus groups were conducted with 69 participants. Fifty-one were South Asian, 13 Caucasian, and five East African. There were 57 females and 12 males aged between 25 and 60 years (average 45). Five themes were identified: knowledge, benefits vs. harms, motivations & deterrents, health information, and awareness. Fifteen interviews were conducted with 21 participants from varied health professions. Four themes were identified: cultural differences, who/how provide information, make education relatable, and make education accessible. Conclusion. Utilizing the feedback from the engagement, a multi-component outreach strategy was co-designed and included a translated awareness-building video series, a social media campaign leveraging partner channels, and a health worker information package with resources to assist with informed cancer screening discussions.
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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".