Health Inequality Monitoring channel on OpenWHO: capacity strengthening through eLearning
Bibliographic record
Abstract
BACKGROUND: Health inequality monitoring can generate important evidence to inform and motivate changes to policy, programmes and practices. However, the potential of health inequality monitoring practices to quantify inequalities between population subgroups and track progress on the advancement of health equity is under-realized. Capacity strengthening on health inequality monitoring can play an important role in enhancing political will for the generation and use of disaggregated data and for wider adoption of this practice to inform health decision-making. There is a lack of widely available and accessible training materials related to health inequality monitoring that may be used by a range of stakeholders. OBJECTIVE: In this paper, we describe the design, development and implementation of the Health Inequality Monitoring channel on the OpenWHO eLearning platform. We discuss the anticipated impact and potential opportunities for these eLearning courses to contribute to strengthened health inequality monitoring practices. RESULTS: The Health Inequality Monitoring channel on the OpenWHO platform is a self-directed learning environment, designed to meet the immediate learning needs of users. The channel contains three series of courses: health inequality monitoring foundations courses; topic-specific health inequality monitoring courses; and health inequality monitoring skill building courses. Courses are primarily targeted to monitoring and evaluation officers, data analysts, academics and researchers, public health professionals, medical and public health students, and others with a general interest in health data and inequality monitoring. CONCLUSIONS: WHO eLearning courses on health inequality monitoring are addressing the need for capacity strengthening in the collection, analysis and reporting of inequality data. They introduce learners to the foundational concepts, best practices, tools and skills required to conduct health inequality monitoring. The courses on the Health Inequality Monitoring channel demonstrate how technical information can be simplified and presented to broad audiences in a manner that is highly accessible to learners. The Health Inequality Monitoring channel on OpenWHO is an innovative and necessary addition to existing tools and resources to support the advancement of health equity.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.007 |
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".