A closer look at the international health regulations capacities in Lebanon: a mixed method study
Bibliographic record
Abstract
BACKGROUND: Lebanon ratified the International Health Regulations (IHR) (2005) in 2007, and since then, it has been facing complex political deadlocks, financial deterioration, and infectious disease emergencies. We aimed to understand the IHR capacities' scores of Lebanon in comparison to other countries, the IHR milestones and activities in Lebanon, the challenges of maintaining the IHR capacities, the refugee crisis's impact on the development of these capacities; and the possible recommendations to support the IHR performance in Lebanon. METHODS: We used a mixed-method design. The study combined the use of secondary data analysis of the 2020 State Party Self-Assessment Annual Report (SPAR) submissions and qualitative design using semi-structured interviews with key informants. Semi-structured interviews were conducted with nine key informants. The analysis of the data generated was based on inductive thematic analysis. RESULTS: According to SPAR, Lebanon had levels of 4 out of 5 (≤ 80%) in 2020 in the prevention, detection, response, enabling functions, and operational readiness capacities, pertaining that the country was functionally capable of dealing with various events at the national and subnational levels. Lebanon scored more than its neighboring countries, Syria, and Jordan, which have similar contexts of economic crises, emergencies, and refugee waves. Despite this high level of commitment to meeting IHR capacities, the qualitative findings demonstrated several gaps in IHR performance as resource shortage, governance, and political challenges. The study also showed contradictory results regarding the impact of refugees on IHR capacities. Some key informants agreed that the Syrian crisis had a positive impact, while others suggested the opposite. Whether refugees interfere with IHR development is still an area that needs further investigation. CONCLUSION: The study shows that urgent interventions are needed to strengthen the implementation of the IHR capacities in Lebanon. The study recommends 1) reconsidering the weight given to IHR capacities; 2) promoting governance to strengthen IHR compliance; 3) strengthening the multisectoral coordination mechanisms; 4) reinforcing risk communication strategies constantly; 5) mobilizing and advancing human resources at the central and sub-national levels; 6) ensuring sustainable financing; 7) integrating refugees and displaced persons in IHR framework and its assessment tools; 8) acknowledging risk mapping as a pre-requisite to a successful response; and 9) strengthening research on IHR capacities in Lebanon.
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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 | 0.027 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".