Case 9 : A Knot of Contradictions: Systems of Intersectionality and Muslim LGBTQ+ Mental Health Programs
Bibliographic record
Abstract
The case involves the protagonist, Yasmin Baytar, a queer Muslim woman who returns from the 2019 Women Deliver conference with the goal of implementing a community mental health program focusing on the LGBTQ+ Muslim population in Ottawa. She has extensive Sex- and Gender-Based Analysis Plus training and knowledge about intersectionality that she can use to develop a program that embraces true equality. However, she also needs to figure out how to obtain funding for her program and collaborate with different stakeholders while making sure she is keeping her population of interest involved and at the centre of her work. Students must use systems thinking approaches and recognize the importance of intersectionality when building the community mental health program. Incorporating an element of cultural sensitivity/competency into the program will show the students’ ability to critically think about an issue while taking intersecting identity factors into account. Furthermore, recognizing the importance of various levels of intervention, students will use the Social Ecological Model to ensure a multipronged, multileveled approach is included as the program is built. Students will be able to collaborate with a variety of experts/stakeholders to ensure the success of the intervention.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.023 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 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".