Muslim immigrant women’s views on cervical cancer screening and HPV self-sampling in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Canada has observed significant decreases in incidence and mortality of cervical cancer in recent decades, and this has been attributed to appropriate screening (i.e., the Pap test). However, certain subgroups including Muslim immigrants show higher rates of cervical cancer mortality despite their lower incidence. Low levels of screening have been attributed to such barriers as lack of a family physician, inconvenient clinic hours, having a male physician, and cultural barriers (e.g., modesty, language). HPV self -sampling helps to alleviate many of these barriers. However, little is known about the acceptability of this evidence-based strategy among Muslim women. This study explored Muslim immigrant women's views on cervical cancer screening and the acceptability of HPV self-sampling. METHODS: An exploratory community-based mixed methods design was used. A convenience sample of 30 women was recruited over a 3-month period (June-August 2015) in the Greater Toronto Area. All were between 21 and 69 years old, foreign-born, self-identified as Muslim, and had good knowledge of English. Data were collected through focus groups. RESULTS: This study provides critical insights about the importance of religious and cultural beliefs in shaping the daily and health care experiences of Muslim women and their cancer screening decisions. Our study showed the deterring impact of beliefs and health practices in home countries on Muslim immigrant women's utilization of screening services. Limited knowledge about cervical cancer and screening guidelines and need for provision of culturally appropriate sexual health information were emphasized. The results revealed that HPV self-sampling provides a favorable alternative model of care to the traditional provider-administered Pap testing for this population. CONCLUSION: To enhance Muslim immigrant women screening uptake, efforts should made to increase 1) their knowledge of the Canadian health care system and preventive services at the time of entry to Canada, and 2) access to culturally sensitive education programs, female health professionals, and alternative modes of screening like HPV self-sampling. Health professionals need to take an active role in offering screening during health encounters, be educated about sexual health communication with minority women, and be aware of the detrimental impact of preconceived assumptions about sexual activity of Muslim women.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".