Tracking emergency response actions during COVID-19 leads to development of an innovative public health evaluation tool
Bibliographic record
Abstract
SETTING: Early in the pandemic, KFL&A Public Health needed a way to capture, organize, and display COVID-19-related events to be accountable for and evaluate our actions. INTERVENTION: We used accessible software (Microsoft Office 365 suite, Microsoft PowerBI) to develop a data collection and visualization system. The Canadian Institute for Health Information (CIHI) developed a timeline and categorization approach for provincial and national COVID-related interventions, which was used to develop a regional version for local events using similar categories. We collected and displayed qualitative data alongside epidemiological data that allowed users to display different timelines of actions and outcomes and evaluate our response. OUTCOMES: In developing the timeline, we took stock of the information and data we wanted to collect, sort, and display locally. Next, we collected information on response actions, case and contact tracing, and staffing changes in a database that we displayed on a timeline. We included CIHI's data set to provide insight into pandemic response across all jurisdictions. IMPLICATIONS: Our timeline tool has many advantages for public health authorities beyond responding to a rapidly evolving emergency. By collecting information on events as they occur, decisions and actions are documented that may otherwise be overlooked. This enables decision-makers to visualize the impact of public health actions on health outcomes over time. The tool is completely customizable and scalable depending on the project scope and we plan to apply this method to other public health programming. Finally, we include lessons learned from quickly developing these tools in a real-time pandemic setting, both locally at KFL&A Public Health and nationally at CIHI.
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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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".