“Health Technology Assessment in High, Middle and Low-income Countries: New Systematic and Interdisciplinary Approach for Sound Informed-policymaking”
Bibliographic record
Abstract
Abstract Technological innovation has a significant role in improving health systems (HSs) and achieving universal health coverage. 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 in order to provide evidence to leaders and ensuring the value of resources by evaluating properties, effects, and/or impacts. The scope of HTA focuses on conducting assessments and analyses to investigate the medical, social, economic, organizational and ethical issues within health and social systems for generating management and technical solutions. HTA is important as it is rapidly growing and is seen as an essential development approach to tackle existing challenges, particularly in developing countries as they share most of the health burdens worldwide. The research aims to comprehensively evaluate HTA within the health and social systems and understand HTA within the national health system with regards to the level of knowledge about HTA, current HTA practices, application, capacity, gaps, and solutions by investigating the perceptions of health systems’ stakeholders in five countries, Canada, Switzerland, Lebanon, Palestine, and Tanzania selected according to the World Bank income classification. The project will last 12 months starts in January 2021 and ends in January 2022. A mixed-methods, quantitative and qualitative, along with a scoping review will be applied. In each country, fifty semi-structured questionnaires, twenty in-depth interviews, and one national focus group discussion will be conducted with health experts, managers, and policymakers selected purposively from the 1st and 2nd levels of the HS structure. Excel, IBM Statistical Package for the Social Sciences (SPSS), and MAXQDA 12 (VERBI GmbH, Berlin) software programs will be used for data management and analysis. The research will form cutting-edge evidence and reference not only for the six countries, but also for the global, regional, and national endeavors with regards to opening a room for HTA best application and optimization based on the produced knowledge from this research. It will reveal lessons learned, determine gaps, and set an applicable strengthening framework for HTA. This framework will eventually aid the decision and policymakers in these countries, and other similar countries and international organizations to build a well-enabled and institutionalized HTA for better universal health coverage, 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 | 0.372 | 0.285 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".