An analysis of the inclusion of ear and hearing care in national health policies, strategies and plans
Bibliographic record
Abstract
Ear- and hearing-related conditions pose a significant global health burden, yet public health policy surrounding ear and hearing care (EHC) in low- and middle-income countries is poorly understood. The present study aims to characterize the inclusion of EHC in national health policy by analysing national health policies, strategies and plans in English, French, Spanish, Portuguese and Arabic. Three EHC keywords were searched, including ear*, hear* and deaf*. The terms 'human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS)', 'tuberculosis' and 'malaria' were included as comparison keywords as these conditions have historically garnered political priority in global health. Of the 194 World Health Organization Member States, there were 100 national policies that met the inclusion criteria of document availability, searchable format, language and absence of an associated national EHC strategy. These documents mentioned EHC keywords significantly less than comparison terms, with mention of hearing in 15 documents, ears in 11 documents and deafness in 3 documents. There was a mention of HIV/AIDS in 92 documents, tuberculosis in 88 documents and malaria in 70 documents. Documents in low- and middle-income countries included significantly fewer mentions of EHC terms than those of high-income countries. We conclude that ear and hearing conditions pose a significant burden of disease but are severely underrepresented in national health policy, especially in low- and middle-income countries.
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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 | 0.023 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".