Mobilizing a Whole Community: Policy and Strategy Implications of an Integrated Local System Response to a Global Health Crisis
Bibliographic record
Abstract
The East Toronto Health Partners (ETHP) include more than 50 organizations working collaboratively to create an integrated system of care in the east end of Toronto. This existing partnership proved invaluable as a platform for a rapid, coordinated local response to the COVID-19 pandemic. Months after the first wave of the pandemic began, with the daily numbers of COVID-19 cases finally starting to decline, leaders from ETHP provided preliminary reflections on two critical questions: (1) How were existing integration efforts leveraged to mobilize a response during the COVID-19 crisis? and (2) How can the response to the initial wave of COVID-19 be leveraged to further accelerate integration and better address subsequent waves and system improvements once the pandemic abates?
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.
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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".