A one health approach for integrated vector management monitoring and evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The French Agency for Food, Environmental and Occupational Health & Safety (Anses) has set up a multidisciplinary working group (WG) to develop an innovative One Health approach for the monitoring and evaluation of an integrated vector management system (IVMS) on a territorial scale. Four existing evaluation guidelines and methods have been combined into a semi-quantitative evaluation approach that takes into account all the dimensions of an integrated process. We propose a set of 34 criteria divided into three sections (objectives and management, implementation, integration) that correspond to the main functional components of an IVMS. Each criterion is assigned a score based on the results of a scoring questionnaire completed by the system's stakeholders, and two graphical outputs are generated using a specific combination of these scores. An overview of the system's performance is provided through a series of pie charts synthesizing the scores for each of the three sections and the corresponding eleven subsections. A radar chart further combines the results according to eight attributes chosen to characterize the qualities of the system. Our approach was tested for the invasive mosquito Aedes albopictus, a main vector of arboviruses, in two French territories with contrasting dengue epidemiology. This approach is intended to be generic and usable in all territories that are at risk of being affected by arboviruses, whether in tropical or temperate regions. Beyond a conventional assessment of the various components of an IVMS, our interdisciplinary and multisectoral approach aims to gain a better understanding of such a system in its environment, its overall functioning and its mechanisms for adapting to contextual change. It also aims to identify avenues for improvement as part of a continuous quality process, and to facilitate comparisons between territories and the cross-fertilization of knowledge between stakeholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it