Facing FAQs: H1N1 and Homelessness in Toronto
Bibliographic record
Abstract
The homelessness sector of Toronto faced a public health threat from the H1N1 pandemic. This report shares the findings of research undertaken in 2010 and 2011, assessing the pandemic preparedness of the homelessness sector before, during, and after the outbreak. Interviews were conducted with 149 homeless individuals, fifteen social service providers, and five key stakeholders involved in the H1N1 response. \n \nThis report is divided into five key sections, and uses a question and answer approach to examine the core issues: \n \n1. “Homelessness, Health and Infrastructure in Toronto” examines how the homelessness sector is organized, how well homeless individuals are faring mentally and physically within the city, and how the sector organizes health care services for its clients. \n \n2. “Preparing the Homelessness Sector for H1N1” explores the work that was done prior to the outbreak and the challenges that arose. \n \n3. “H1N1 and the Homelessness Sector Response” examines how the sector performed during the outbreak phase. Included in this section are discussions of operational changes, communication strategies, supplies, vaccination efforts, and infection control measures. \n \n4. “Learning from H1N1” offers a reflection on how prepared the sector is for another outbreak and what challenges would need to be overcome in the event of a more severe pandemic. This report ends with a “Conclusion and Recommendations” section that pulls the key findings together and offers recommendations for creating a more integrated and interconnected sector.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".