A Critical Lens on Health: Key Principles of Critical Discourse Analysis and Its Benefits to Anti-Racism in Population Public Health Research
Bibliographic record
Abstract
Critical discourse analysis (CDA) is an interdisciplinary research methodology used to analyze discourse as a form of “social practice”, exploring how meaning is socially constructed. In addition, the methodology draws from the field of critical studies, in which research places deliberate focus on the social and political forces that produce social phenomena as a means to challenge and change societal practices. The purpose of this article is to demonstrate the benefits of CDA to population public health (PPH) research. We will do this by providing a brief overview of CDA and its history and purpose in research and then identifying and discussing three crucial principles that we argue are crucial to successful CDA research: (1) CDA research should contribute to social justice; (2) CDA is strongly based in theory; and (3) CDA draws from constructivist epistemology. A key benefit that CDA brings to PPH research is its critical lens, which aligns with the fundamental goals of PPH including addressing the social determinants of health and reducing health inequities. Our analysis demonstrates the need for researchers in population public health to strongly consider critical discourse analysis as an approach to understanding the social determinants of health and eliminating health inequities in order to achieve health and wellness for all.
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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 | 0.111 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.018 | 0.179 |
| Scholarly communication | 0.033 | 0.032 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.011 | 0.012 |
| 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, 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".