Promoting Cancer Screening Literacy through Faith-Based Organizations: Perspectives of Muslim Imams
Bibliographic record
Abstract
Background: Immigrants tend to have lower rates of cancer screening. Inequity in screening rates may stem from socio-cultural barriers and religious misinterpretations, both of which can be influenced by faith-based organizations and religious leaders. The aim of this study was to explore the knowledge and attitudes held by Muslim religious leaders, or Imams, about cancer screening, as well as the role they perceive they can play in improving cancer screening health literacy among South Asian Muslim immigrant women. Methods: We conducted interviews with eight imams from faith-based organizations in Calgary, Canada. Participants’ knowledge and attitudes were inductively summarized using descriptive analysis, while practices were deductively thematically analyzed using the Socioecological Model and the Communication for Development approach. Results: We found participants mostly had some knowledge of cancer but lesser knowledge of different screening tests and of low screening rates among immigrants. Participants identified a lack of information about the healthcare system as the major barrier to cancer screening among immigrants and discussed the impact of culture and religion on screening. Participants proposed that their role as a speaker in the community, role in faith-based organizations, access to large facilities and crowds, and collaboration with universities and healthcare professionals would be fruitful in promoting cancer screening among South Asian Muslim immigrant women. Conclusion: Imams were highly supportive of incorporating health messaging into faith-based messaging. Future work should focus on collaboration between religious leaders and health professionals as recommended in this study.
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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 | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".