Applying Intersectionality With Constructive Grounded Theory as an Innovative Research Approach for Studying Complex Populations: Demonstrating Congruency
Bibliographic record
Abstract
One goal of qualitative health research is to fully capture and understand stories of people who experience inequities shaped by complex interlocking structural and social determinants. With this social justice–oriented goal in mind, it is critical to use a methodological approach that appreciates prevailing inequities and oppression. In this article, we propose an innovative approach that joins qualitative health research methodology with critical inquiry. Specifically, we propose advancing constructive grounded theory (CGT) through applying intersectionality as an emergent critical social theory and an analytical tool. With our proposed approach being novel, minimal attempts to conceptualize and operationalize CGT with intersectionality exist. This article focuses on initiating theoretical conceptualization through focusing on demonstrating congruency. We are guided by this focus to seek connectedness and fit through analyzing historical and philosophical assumptions of CGT and intersectionality. In our article, we demonstrate congruency within four units of analysis: reflexivity, complexity, variability, and social justice. Through these units, we offer implications to applying intersectionality within CGT methodology. These include a foundation that guides researchers toward further conceptualizing and operationalizing this novel research approach. Implications also include innovatively exploring complex population groups who face structural inequities that shape their lived vulnerabilities. Our proposed research approach supports critical reflection on the research process to consider what shapes the researcher–participant relationship. This includes reflecting on analysis of power dynamics, underlying ideologies, and intermingling social locations. Thus, our conceptual paper addresses the call for evolving social justice methodologies toward inquiring into complex populations and generating knowledge that challenges and resists inequity.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.196 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.011 | 0.080 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".