Bibliographic record
Abstract
ARS was a new, unknown disease that was highly contagious within hospitals and was difficult to diagnose.For the first time in more than 60 years, the province declared a "code orange" in an effort to control the spread of the disease.No one could remember dealing with anything like this before.The disease posed a serious challenge for the healthcare field, not just from the point of view of how we provide care but also how we communicate with hospital staff and the public.Internationally recognized infection control specialists Drs.Don Low and Allison McGeer put Mount Sinai Hospital at the forefront of this battle.They played a dual role, not only treating patients and helping formulate provincial directives related to infection control measures, but also acting as key media spokespeople.Because of their ability to translate medical information into a context everyone could understand, they were in high demand for media interviews.Mount Sinai Hospital received up to 100 media inquiries on any given day from cities around the world.As the media hype grew, it created an unexpected backlash against healthcare workers.SARS became a "hospital" disease.For the first time ever, healthcare workers felt vulnerable.Not only were they concerned about catching the disease as they treated patients but they also had to cope with being a target of negative public reaction.They were often asked not to attend family functions, take their children to daycare, or show up for hair cut or dental appointments for fear they were contagious.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.015 | 0.034 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".