“Treat Me Like a Person”: Unveiling Healthcare Narratives of Muslim Women who Wear Islamic Head Coverings Through a Poststructural Narrative Study
Bibliographic record
Abstract
BACKGROUND: In Canada, the healthcare experiences and needs of Muslim women who wear Islamic head coverings are conflated with the larger Muslim community who do not wear Islamic head coverings. Understanding their specific and unique preferences and challenges is essential for tailoring care and improving healthcare encounters. PURPOSE: The study purpose is to explore the healthcare encounters of Muslim women wearing Islamic head coverings in Canada, focusing on how discourse influences their narratives. METHODS: A postructuralist narrative methodology was used to understand how power, knowledge, language, and discourse impacted their experiences. Semi-structured interviews were conducted with eight Muslim women. Narrative analysis was used to dissect stories and the way these stories were told. RESULTS: Five themes were identified, including: The Fingerprint: Highlights the importance of recognizing individual identities to provide personalized care.The Membrane: Examines how societal biases and assumptions permeate healthcare professionals and impacts care.The Heartbeat: Reveals the immediate emotional and physical responses that reflect systemic challenges within healthcare encounters.Unseen: Emphasizes the lack of acknowledgement experienced by Muslim women related to their healthcare preferences and/or needs.Heard: Encompasses instances where Muslim women feel recognized by their healthcare provider; contrasts Theme #4. CONCLUSION: This research emphasizes the diverse experiences of Muslim woman who wear an Islamic head covering and the need for healthcare professionals to move away from a one-size-fits-all approach and instead, provide care that respects the unique preferences amongst this diverse group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".