Evaluating predictive factors for engaging in positive breast health behaviours: A quantitative study among immigrant women in Montreal
Bibliographic record
Abstract
Introduction: Breast cancer is the most common cancer in women worldwide; however, Iranian breast cancer patients are relatively younger than their counterparts in developed countries. This is important for Canada for two reasons. First, Iran is one of the ten most common countries of birth among immigrants with women making up over half of that population. Second, immigrant women in general are often marginalized and isolated due to inadequate language skills, lack of knowledge of services offered to immigrants, difficulties integrating, not knowing how to access breast cancer screening and about positive breast health behaviour, as well as religious and cultural barriers. Of the limited empirical evidence on breast cancer screening among newly landed immigrants, few studies have evaluated behaviours and beliefs pertaining to breast cancer and breast health behaviour including breast cancer screening practices in Iranian women. Objectives: To evaluate self-reported engagement in positive breast health behaviours (breast self- examination, clinical breast exam, or mammography) among Iranian immigrant women. The specific aims are to further determine what are the self-identified barriers and facilitators for engaging in breast health behaviour and to determine what factors are associated with an increased likelihood of engaging in breast health behaviour in this population.Methods: Several conceptual frameworks were incorporated to guide the study data collection tools for this thesis including Andersen's Behavioral Model of Health Service Use, Champion's Health Belief Model, and Berry's Acculturation Model. This was a cross-sectional study conducted in Montreal area from August 2017 until February 2018. A self-administered questionnaire was distributed to 450 Iranian immigrant women residing in Montreal with snowball sampling. The inclusion criteria for the participants were: a) women between the ages of 20-80 years who lived in Montreal, b) with no history of breast cancer, and c) able to read and speak English, French, and/or Persian. Using SPSS, data were analyzed through descriptive univariate frequencies, bivariate cross tabulation and multivariate logistic regression. Logistic regression was used to identify predictors of women engaging in breast health behaviour. I conducted the method of backward stepwise regression to establish the final logistic regression model (p>0.20 for removal from model). The logistic regression results were reported as odds ratios (ORs) and 95% confidence intervals. Results: In general, screening rates for breast cancer among the 403 participants in the current study were higher than those reported for Canadian women nationally. The majority of women heard about breast cancer (86%), and 91% engaged in at least one positive breast health behaviour. Among predictors, only age, breast self-examination self-efficacy, having history of breast problems, and receiving doctor's recommendation had strong association with positive breast health behaviour [adjusted OR (95% Confidence Interval(CI)]. Acculturation and religiosity were not significantly correlated to positive breast health behaviour. Conclusion: These findings are inconsistent with previous studies on immigrant women living in developed countries. Eliminating barriers, promoting motivators, and increasing perceived self-efficacy; as well as persuading physicians to provide recommendations for mammography are important to promote breast cancer screening among Iranian immigrant 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 | 0.003 | 0.001 |
| 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".