How do World Health Organization technical officers working on noncommunicable diseases approach health equity?
Bibliographic record
Abstract
INTRODUCTION: Health equity has become a common objective in both global and public health. Although there has been recent scholarship to critically examine how this concept has been applied by the World Health Organization (WHO), there has not been any detailed investigation into the WHO's work on noncommunicable diseases (NCDs). This study aims to fill this gap by investigating the approaches taken by WHO technical officers working on NCDs to address health equity. METHODS: The perspectives of technical officers working on NCDs at the WHO were collated through semi-structured key informant interviews. Interviews were transcribed verbatim to facilitate data analysis by two independent reviewers in NVivo 14. RESULTS: Key informants felt: a disconnect between NCDs programmatic efforts and health equity; that equity in health primarily involves ensuring equitable access to healthcare, with an emphasis on addressing financial hardship; and that equity in health entails targeting those who are most 'vulnerable'. In investigating how health equity is being applied in NCD efforts, it was apparent that a consideration of health equity is missing in program implementation and policy design, and that donors' goals supersede long-term prevention efforts. Lastly, in pinpointing concrete changes or results seen around how health equity is operationalized in NCDs efforts, several success stories from various regions emerge. CONCLUSION: The findings of this study demonstrate that WHO technical officers working on NCDs often possessed a limited understanding of health equity that resulted in little meaningful action to embed health equity considerations into programmatic and policy work. Evidently, WHO technical officers need to better navigate or contest industry interference and learn more about health equity as a concept and the links to NCDs.
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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 | 0.044 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".