Challenges of Inclusion of Muslim Female Students in Social Work Programs in Western Canada
Bibliographic record
Abstract
Introduction: Canada is known globally as a multicultural country, yet multiple studies have indicated that Muslim Canadians believe discrimination, based on their religious beliefs, is common. What is less well-understood is the experience of discrimination of Muslim students in post-secondary settings, particularly in social work. Research Questions: What are the experiences of Muslim female students with regards to discrimination inclusion/exclusion, social justice, and equity in social work programs in Western Canada? How do the experiences of Muslim female students in social work programs in Western Canada impact their lives? Methods: The study used a qualitative descriptive phenomenological design. Twelve Muslim female students currently enrolled in or recent graduates (within the past two years) of university social work programs in Western Canada were recruited using convenience sampling methods. Semi-structured exploratory interviews were conducted between September and November 2020 and the phenomenological approach was used for data analysis. Results: Study participants identified experiences of exclusion, stereotyping, and discrimination. A number of challenges, such as the lack of diversity, lack of awareness by professors and students were faced by Muslim female students. These findings could better inform educators and policymakers in formulating culturally appropriate policies to increase diversity and inclusion and to promote intercultural education in Canadian Universities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".