Use of Intersectionality Theory and Interpretive Descriptive Qualitative Method to Address Inequalities in Marginalized Communities
Bibliographic record
Abstract
To understand the application of Intersectionality theory in conducting qualitative interpretative research on exploring inequalities and discriminatory healthcare practices towards marginalized communities. Method: Narrative Review. Data Sources: Systematic Literature search. Findings: Qualitative research on exploring discriminatory healthcare practices towards marginalized communities requires a research methodology that is practice-oriented and flexible in using theoretical knowledge. The theory of intersectionality exposes how socially constructed identities are intertwined with discriminatory healthcare practices toward marginalized communities. On the other hand, Interpretative Description (ID) is a clinical-based qualitative methodology that aims to identify gaps in healthcare and nursing practices and create evidence-based interventions to address such gaps. Using intersectionality with ID methodology allows researchers to identify discriminatory healthcare practices towards racialized communities and create resources to provide equalized care to marginalized communities. Conclusion: The theory of Intersectionality provides theoretical scaffolding to understand the impact of power, race, and social identities on marginalized populations. Using ID with intersectionality theory will be a novel approach to conducting research on marginalized communities and identifying ways to address the inequalities in nursing and healthcare practices.
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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 | 0.121 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".