Bibliographic record
Abstract
SARS was the first novel infectious disease to emergein the 21st century. Its dramatic appearance in majorcities around the world, together with the fact that 20 % of the 8400 infected individuals were health care workers,1 prompted epidemiologists and other scientists to move swiftly to study the disease and identify its causal agent.2,3 We now know that SARS is associated with a pre-viously unrecognized virus, SARS-CoV.4 Rapid diagnostic tests using the polymerase chain reaction are being devel-oped,5 and the treatment regimens used in the outbreaks of 2002–2003 continue to be reviewed and evaluated.6 It is interesting that, even during the height of the out-breaks, researchers were also trying to understand and measure the psychosocial effects of SARS.7–9 Compared with the available literature on the biology of infectious dis-eases, there have been considerably fewer published reports on the psychosocial impact of SARS and other disease out-breaks. Thus, the article in this issue by Nickell and col-leagues10 (see page 793) makes an important contribution toward a better understanding of this often neglected area. Their study was carried out in a large teaching hospital in Toronto in April 2003, during the peak of the first phase of the SARS outbreak in the city. The authors found signif-icant levels of psychiatric morbidity, in that almost two-thirds of the staff surveyed reported increased levels of con-cern for personal and family health, and almost one-third of a subset of respondents who completed a 12-item Gen-eral Health Questionnaire had scores indicating emotional distress. Their findings are consistent with those of studies on SARS in Taiwan, where up to 75 % of health care work-ers experienced psychiatric morbidity (Dr. Mian-Yoon
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.342 | 0.124 |
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".