Analysing the Health of Queer Muslims Through the 4M Framework: A Scoping Literature Review
Bibliographic record
Abstract
Abstract The health and wellbeing of queer Muslims, a group positioned at the intersection of multiple marginalised identities, remains underexplored in academic literature. This scoping literature review critically analyses existing research on queer Muslim health using the 4M framework (Mega, Macro, Meso, Micro) to identify structural and individual determinants impacting health outcomes. The study highlights the profound influence of intersecting factors such as race, ethnicity, gender, sexuality, geographic location, and socioeconomic status on healthcare access and health outcomes. Findings reveal that dominant epistemological assumptions about queerness and Islam perpetuate stigma, discrimination, and minority stress, leading to adverse health outcomes. Key barriers include inadequate funding, homonormative healthcare policies, and exclusionary cultural expectations within healthcare settings. Conversely, supportive familial, peer, and religious networks, along with access to digital resources, are identified as facilitators of better health outcomes. The review calls for culturally competent, strength-based models of care and emphasises the need for future research to address the diverse health experiences of queer Muslims across different regions and identities.
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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 | 0.021 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".