We are what we are: Religious discrimination and oral health care among Muslim people in Montreal
Bibliographic record
Abstract
Background: The Muslim people are the second-largest religious group in Canada and represent approximately 3.2 percent of the population.Since the Bouchard-Taylor commission on ''reasonable accommodation'' of minorities in Quebec, Muslim people have been at the forefront of the debate on religious signs and symbols.They have also faced discrimination and even violence; the Quebec City mosque shooting in 2017 being an example.While there is a large body of research on the impact of racial and ethnic discrimination on physical and mental health, very little is known about the impact of religious discrimination.In particular, we know very little about how experiencing religious discrimination may affect people's oral health and oral health-related behaviors.Research Question: How does religious discrimination affect the oral health care of Muslim people?Methodology: We conducted a qualitative exploratory study within the Muslim people of Montreal, Quebec.We adopted a maximum variation sampling strategy regarding age, sex, education level, marital status, and employment status.We collected our data through face-to-face, in-depth, semi-structured interviews of 11 participants practicing Islam.The interviews were audio-recorded and transcribed verbatim to be analyzed.Data analysis was carried out by using a thematic analysis approach, which allowed us to provide a detailed description of the phenomenon. Results:The participants experienced religious discrimination in three different settings, most commonly in daily life but also in the healthcare and the dental healthcare systems.The participants experienced discrimination in the form of verbal, non-verbal, physical aggression related to Islamic clothing and prayer rituals in public places (metro station, streets, bus, etc.).These experiences had a major impact on their life, including stress and anxiety, feelings of insecurity, and, in some cases, emigration to escape discrimination.Some participants experienced negative behaviors and disinterest from healthcare and non-healthcare professionals (receptionists), which generated a lack of trust in healthcare professionals in general.Three participants had similar experiences during their dental visits that led them to lose trust in dentists and, ultimately, avoid dental visits.Conclusion: In sum, our findings showed that religious discrimination had an impact on the mental health of the Muslim people through stress and anxiety.It may have an impact on the use of dental care services and access to care.And this, in turn, may negatively impact oral health.We were
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".