Investigating inconsistencies regarding health equity in select World Health Organization texts: a critical discourse analysis of health promotion, social determinants of health, and urban health texts, 2008–2016
Bibliographic record
Abstract
BACKGROUND: Scholarly critiques have demonstrated that the World Health Organization (WHO) approaches the concept of health equity inconsistently. For example, inconsistencies center around measuring health inequity across individuals versus groups; in approaches and goals sought in striving for health equity; and whether considerations around health equity prioritize socioeconomic status or also consider other social determinants of health. However, the significance of these contrasting approaches has yet to be assessed empirically. METHODS: This study employs critical discourse analysis to assess the WHO's approaches to health equity in select health promotion, social determinants of health, and urban health texts from 2008 to 2016. RESULTS: We find that the WHO: (i) usually measures health equity by comparing groups; (ii) explicitly specifies three approaches to health equity (although we identified additional implicit approaches in our analysis of WHO discourses); and (iii) considers health equity inconsistently both in terms of socioeconomic status and other social determinants of health, but socioeconomic status was given substantially more attention than other individual social determinants of health. CONCLUSIONS: There is misalignment with the WHO's stated approaches to tackle health inequity and its discourses around health equity. This incongruence increases the likelihood of pursuing short-term solutions and not sustainably addressing the root causes of health inequity. Critical discourse analysis' focus on power allows for understanding why 'radical' approaches are not explicitly expressed to ensure that governments will be agreeable to addressing health inequity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 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".