Health inequities in Canada : intersectional frameworks and practices
Bibliographic record
Abstract
Introduction: Purpose, Overview, and Contribution / Olena Hankivsky, Sarah de Leeuw, Jo-Anne Lee, Bilkis Vissandjee, and Nazilla Khanlou 1 Why the Theory and Practice of Intersectionality Matter to Health Research and Policy / Rita Kaur Dhamoon and Olena Hankivsky Part 1: Theoretical and Methodological Innovations / Edited by Sarah de Leeuw and Olena Hankivsky 2 Beyond Borders and Boundaries: Addressing Indigenous Health Inequities in Canada through Theories of Social Determinants of Health and Intersectionality / Sarah de Leeuw and Margo Greenwood 3 A Cross-Cultural Quantitative Approach to Intersectionality and Health: Using Interactions between Gender, Race, Class, and Neighbourhood to Predict Self-Rated Health in Toronto and New York City / Jennifer Black and Gerry Veenstra 4 Performing Intersectionality: The Mutuality of Intersectional Analysis and Feminist Participatory Action Health Research / Colleen Reid, Pamela Ponic, Louise Hara, Connie Kaweesi, and Robin LeDrew 5 Adding Religion to Gender, Race, and Class: Seeking New Insights on Intersectionality in Health Care Contexts / Sheryl Reimer-Kirkham and Sonya Sharma Part 2: Intersectionality Research across the Life Course / Edited by Nazilla Khanlou and Olena Hankivsky 6 Navigating the Crossroads: Exploring Young Women's Experiences of Health Using an Intersectional Framework / Natalie Clark and Sarah Hunt 7 Exploring Health and Identity through Photovoice, Intersectionality, and Transnational Feminisms: Voices of Racialized Young Women / Jo-Anne Lee and Alison Sum 8 An Intersectional Understanding of Youth Cultural Identities and Psychosocial Integration: Why It Matters to Mental Health Promotion in Immigrant-Receiving Pluralistic Societies / Nazilla Khanlou and Tahira Gonsalves 9 Adopting an Intersectionality Perspective in the Study of the Healthy Immigrant Effect in Mid- to Later Life / Karen M. Kobayashi and Steven G. Prus 10 Intersectionality in the Context of Later Life Experiences of Dementia / Wendy Hulko Part 3: Social Context, Policy, and Health / Edited by Bilkis Vissandjee and Olena Hankivsky 11 An Intersectional Lens on Various Facets of Violence: Access to Health and Social Services for Women with Precarious Immigration Status / Jacqueline Oxman-Martinez and Jill Hanley 12 Place, Health, and Home: Gender and Migration in the Constitution of Healthy Space / Parin Dossa and Isabel Dyck 13 Preventing and Managing Diabetes: At the Intersection of Gender, Ethnicity, and Migration / Bilkis Vissandjee and Ilene Hyman 14 Intersectionality Model of Trauma and Post-Traumatic Stress Disorder / Joan Samuels-Dennis, Annette Bailey, and Marilyn Ford-Gilboe Part 4: Disrupting Power and Health Inequities / Edited by Jo-Anne Lee and Olena Hankivsky 15 Addressing Trauma, Violence, and Pain: Research on Health Services for Women at the Intersections of History and Economics / Annette J. Browne, Colleen Varcoe, and Alycia Fridkin 16 Intersectional Frameworks in Mental Health: Moving from Theory to Practice / Katherine R. Rossiter and Marina Morrow 17 Intersectionality, Justice, and Influencing Policy / Colleen Varcoe, Bernadette Pauly, and Shari Laliberte 18 Intersectional Feminist Frameworks in Practice: CRIAW's Journey toward Intersectional Feminist Frameworks, Implications for Equity in Health / Jo-Anne Lee Afterword / Olena Hankivsky List of Contributors Index
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.031 |
| Science and technology studies | 0.038 | 0.025 |
| Scholarly communication | 0.032 | 0.009 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".