Developing an evaluation framework for public health environmental surveillance: Protocol for an international, multidisciplinary e-Delphi study
Bibliographic record
Abstract
Abstract Introduction Public health environmental surveillance has evolved, especially during the coronavirus disease pandemic, with wastewater-based surveillance being a prominent example. As surveillance methods diversify and expand, it is essential to have a robust evaluation of surveillance systems. This electronic Delphi study will propose an evaluation framework for public health environmental surveillance, informed by the expanding practice of wastewater-based surveillance during the pandemic. Methods The Public Health Environmental Surveillance Evaluation Framework (PHES-EF) will be developed in five steps. In Step 1, a multinational and multidisciplinary executive group will be formed to guide the framework development process. In Step 2, candidate items for Round 1 of the electronic Delphi will be generated by conducting relevant scoping reviews and consultation with the study executive group. In Step 3, an international electronic Delphi will be conducted over two rounds to develop consensus on items for the framework. In Step 4, the executive group will reconvene to finalize the evaluation framework, discuss standout items, and determine the dissemination strategies. Lastly, Step 5 will focus on disseminating the evaluation framework to all parties involved with or affected by wastewater-based surveillance using traditional and public-oriented methods. Discussion The electronic Delphi study will provide multidisciplinary and multinational consensus for the evaluation framework for public health environmental surveillance by providing a set of minimum criteria required for their evaluation. The evaluation framework is intended to support the sustainability of environmental surveillance and improve its implementation, reliability, credibility, and value.
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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 | 0.305 | 0.219 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".