Lessons learned from implementing a surge capacity support program for COVID-19 contact management in Ontario
Bibliographic record
Abstract
SETTING: In Ontario, local public health units (PHUs) are responsible for leading case investigations, contact tracing, and follow-up. The workforce capacity and operational requirements needed to maintain this public health strategy during the COVID-19 pandemic were unprecedented. INTERVENTION: Public Health Ontario's Contact Tracing Initiative (CTI) was established to provide a centralized workforce. This program was unique in leveraging existing human resources from federal and provincial government agencies and its targeted focus on initial and follow-up phone calls to high-risk close contacts of COVID-19 cases. By setting criteria for submissions to the program, standardizing scripts, and simplifying the data management process, the CTI was able to support a high volume of calls. OUTCOMES: During its 23 months of operation, the CTI was used by 33 of the 34 PHUs and supported over a million calls to high-risk close contacts. This initiative was able to meet its objectives while adapting to the changing dynamics of the pandemic and the implementation of a new COVID-19 provincial information system. Core strengths of the CTI were timeliness, volume, and efficient use of resources. The CTI was found to be useful for school exposures, providing support when public health measures were lifted, and in supporting PHU's reallocation of resources during the vaccine roll-out. IMPLICATIONS: When considering future use of this model, it is important to take note of the program strengths and limitations to ensure alignment with future needs for surge capacity support. Lessons learned from this initiative could provide practice-relevant knowledge for surge capacity planning.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".