Innovative technology and established partnerships—a recipe for rapid adaptability under emerging pandemic conditions
Bibliographic record
Abstract
Background: Aided by a collaborative partnership dating back to 2011, the Canadian Network for Public Health Intelligence (CNPHI) and the Canadian Paediatric Surveillance Program (CPSP) quickly undertook substantial enhancements to the CPSP's data collection instruments on the CNPHI platform to characterize the impacts of the coronavirus disease 2019 (COVID-19) on children and youth in Canada. Faced with an emerging public health threat with impacts yet unknown, the objective of the intervention was to rapidly complete enhancements to existing data collection and analytical tools to enable the CPSP's ability to characterize the impacts of COVID-19 in Canadian children and youth. Intervention: Reporting frequency from CPSP's network of paediatric practitioners was increased from monthly to weekly, and the flexibility of detailed case data collection was substantially enhanced using complex survey instruments, interactively designed using CNPHI's Web Data technology. To ensure their data collection proceeded along all required lines of surveillance, CPSP's data collection tools were enhanced to collect demographic, epidemiological, microbiological and clinical data including comorbidities of cases identified. Outcomes: Less than a month after the World Health Organization declared the COVID-19 pandemic, CPSP was able to start collecting detailed weekly case data on emerging cases of COVID-19 among Canadian children and youth. By May 2020, CPSP was able to launch a detailed study, supporting research into potential risk factors for severe COVID-19-related illness in children and youth. Conclusion: In response to a novel public health threat, CNPHI and CPSP were able to implement rapid adaptations and enhancements to existing data collection instruments while fortifying their preparedness to do the same in the future, when needed. With innovative and agile technologies at the ready, this experience helps to emphasize the importance of established collaborative partnerships across public health disciplines as a factor contributing to preparedness and agility to respond to the unforeseen. Canadian Network for Public Health Intelligence's Web Data technology showed agile adaptability and a capacity for complex and detailed data collection, supporting timely surveillance and response.
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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 | 0.043 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".