Health Technology Assessment in High, Middle, and Low-income Countries: New Systematic and Interdisciplinary Approach For Sound Informed-policy Making: Research Protocole
Bibliographic record
Abstract
Technological innovation has a significant role in improving health systems (HSs) and achieving universal health coverage (UHC). The World Health Organization (WHO) has declared resolutions on Health Technology Assessment (HTA) and other global organizations emphasized on HTA systems to achieve the Sustainable Development Goals (SDGs). HTA is a modern multidisciplinary decision-making framework linking knowledge and policymaking by assessing the medical, social, economic, organizational, and ethical effects, and/or impacts within health and social systems. The research significance lies in the growing need for HTA at these moments than ever as it is seen as an essential development approach to tackle the current global challenges and pandemics, particularly in developing countries. The research aims to comprehensively evaluate and understand HTA systems concerning the level of knowledge about HTA, current HTA structure, practices, application, capacity, gaps, and solutions. The project starts in January 2021 and ends in January 2022 and will be carried out in seven countries: Canada, Switzerland, Brazil, Lebanon, Jordan, Palestine, and Tanzania. A mixed-methods, quantitative and qualitative, along with a literature review will be applied. In each country, ten HTA-associated institutions will complete an adapted electronic survey, developed by the WHO, and ten key-informants selected purposively from the government, academia, NGOs, and private sectors to participate in ten individual in-depth interviews. One government representative from each country will participate in one expanded inter-country workshop. Excel, IBM Statistical Package for the Social Sciences (SPSS), and MAXQDA software programs will be used for data management and analysis. The research will form cutting-edge evidence not only for the seven countries, but also for the global, regional, and national endeavors with regards to opening a room for HTA best application and optimization. It will reveal lessons learned, determine gaps, and build a well-enabled and institutionalized HTA for better UHC, health systems, and multi-sectoral development.
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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 | 0.199 | 0.189 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.065 | 0.014 |
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".