Needs assessment and gap analysis for national health technology assessments in Lebanon
Bibliographic record
Abstract
Background: Developing countries are increasingly adopting health technology assessment as a critical tool for evaluating and addressing challenges related to the use of health technologies in support of Universal Health Coverage, health equity and resource allocation. Aim: To map health technology assessment resources and identify barriers to national health technology assessments in Lebanon. Methods: Using a questionnaire survey and focus group discussions, we collected data from key informants who had interest in health technology assessment in Lebanon between May and July 2017 and in July 2018. We analysed the quantitative data descriptively using Microsoft Excel and the qualitative data using NVivo version 12. Results: Twenty participants completed the questionnaire and 34 attended the focus group discussions. Participants mentioned reimbursement for the use of health technologies and production of clinical guidelines as top priorities for health technology assessment and mentioned the lack of collaboration nationally and lack of agreement among relevant stakeholders as the topmost barriers to assessments. They mentioned safety and effectiveness as the main parameters to be considered in assessments, followed by quality-of-life and burden of illness. Conclusion: Findings from this study support the need for health technology assessments in Lebanon, beginning with the creation of a national autonomous multidisciplinary entity that will facilitate synergy and consensus-building for the assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".