Health promotion, the social determinants of health, and urban health: what does a critical discourse analysis of World Health Organization texts reveal about health equity?
Bibliographic record
Abstract
BACKGROUND: The World Health Organization (WHO) has focused on health equity as part of its mandate and broader agenda-consider for example, the "health for all" slogan. However, a recent scoping review determined that there are no studies that investigate the WHO's approach to health equity. Therefore, this study is the first such empirical analysis examining discourses of health equity in WHO texts concerning health promotion, the social determinants of health, and urban health. METHODS: We undertook a critical discourse analysis of select texts that concern health promotion, the social determinants of health, and urban health. RESULTS: The findings of this study suggest that (i) underpinning values are consistent in WHO texts' approach to health equity; (ii) WHO texts reiterate that health inequities are socially constructed and mitigatable but leave the 'causes of causes' vague; (iii) despite distinguishing between health "inequities" and "inequalities," there are several instances where these terms are used interchangeably across texts; (iv) WHO texts approach health equity broadly (covering a variety of areas); (v) health equity may be viewed as applicable either throughout the life-course or intergenerationally, which depends on the specific WHO text at hand; and (vi) WHO texts at times use vague or unclear language around how to improve health equity. CONCLUSIONS: This study does not present one definition of health equity and action to be taken. Instead, this study uncovers discourses embedded in WHO texts to spur discussion and deliberate decision-making. This work can also pave the way for further inquiry on other complex key terms or those with embedded values.
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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 | 0.039 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".