Conceptual and institutional gaps: understanding how the WHO can become a more effective cross-sectoral collaborator
Bibliographic record
Abstract
BACKGROUND: Two themes consistently emerge from the broad range of academics, policymakers and opinion leaders who have proposed changes to the World Health Organization (WHO): that reform efforts are too slow, and that they do too little to strengthen WHO's capacity to facilitate cross-sectoral collaboration. This study seeks to identify possible explanations for the challenges WHO faces in addressing the broader determinants of health, and the potential opportunities for working across sectors. METHODS: This qualitative study used a mixed methods approach of semi-structured interviews and document review. Five interviewees were selected by stratified purposive sampling within a sampling frame of approximately 45 potential interviewees, and a targeted document review was conducted. All interviewees were senior WHO staff at the department director level or above. Thematic analysis was used to analyze data from interview transcripts, field notes, and the document review, and data coded during the analysis was analyzed against three central research questions. First, how does WHO conceptualize its mandate in global health? Second, what are the barriers and enablers to enhancing cross-sectoral collaboration between WHO and other intergovernmental organizations? Third, how do the dominant conceptual frames and the identified barriers and enablers to cross-sectoral collaboration interact? RESULTS: Analysis of the interviews and documents revealed three main themes: 1) WHO's role must evolve to meet the global challenges and societal changes of the 21st century; 2) WHO's cross-sectoral engagement is hampered internally by a dominant biomedical view of health, and the prevailing institutions and incentives that entrench this view; and 3) WHO's cross-sectoral engagement is hampered externally by siloed areas of focus for each intergovernmental organization, and the lack of adequate conceptual frameworks and institutional mechanisms to facilitate engagement across siloes. CONCLUSION: There are a number of external and internal pressures on WHO which have created an organizational culture and operational structure that focuses on a narrow, technical approach to global health, prioritizing disease-based, siloed interventions over more complex approaches that span sectors. The broader approach to promoting human health and wellbeing, which is conceptualized in WHO's constitution, requires cultural and institutional changes for it to be fully implemented.
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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 | 0.127 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.026 | 0.072 |
| Scholarly communication | 0.038 | 0.076 |
| Open science | 0.008 | 0.036 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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".