Beyond SARS: ethnic community organization's role in public health — a Toronto experience
Bibliographic record
Abstract
The SARS outbreak in Toronto was a public health crisis. It was particularly frightening to the Chinese-Canadians, because of the origin of the deadly disease. The Chinese-Canadian community organizations launched various activities to help the Chinese-Canadians as well as other Asian-Canadian communities to fight against SARS and its social side-effects. From launching the SARS Supporting Line, distributing health promotional material, disseminating SARS related information, paying tribute to frontline health workers, and promoting local business, to fundraising for SARS related research; they played an active role in easing the public's anxiety, especially for the Chinese-Canadians in the great Toronto area. The culturally diverse population brought problems as well as solutions. Ethnic groups have expertise in almost all areas, including people with leadership skills. The Toronto Chinese community's experience in combating SARS is a good example. The Chinese-Canadian community organizations' activities during the SARS outbreak demonstrate that ethnic minority organizations can play an important role in public health, especially in a public health crisis, and beyond.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".