Bibliographic record
Abstract
The SARS epidemic showed how easy it is for infectious diseases to spread round the world.Ethical as well as clinical issues need to be resolved to improve the response to the next epidemicThe outbreak of severe acute respiratory syndrome (SARS) in the Toronto area earlier this year forced medical and government workers to make hard choices, often with limited information and short deadlines.Healthcare providers were on the firing line, and were the people most affected by the disease. 1 Decision makers had to balance individual freedoms against the common good, fear for personal safety against the duty to treat sick people, and economic losses against the need to contain the spread of a deadly disease.Such decisions have to be guided by both scientific knowledge and ethical considerations.The SARS outbreak showed that Canadian society was not fully prepared to deal with the ethical issues. Evaluating ethical issuesWe formed a working group to identify the key ethical issues and values most important for an analysis of ethical dimensions of the SARS epidemic.The final list of issues and values was agreed by a consensus process and found to have face validity and credibility.We then developed a framework for looking at the ethical implications of the SARS outbreak, identifying 10 key ethical values relevant to SARS (box), and five major ethical issues faced by decision makers.We examined the underlying ethical values for the five major issues and drew lessons from how each was tackled.The following case studies illustrate the issues and are an amalgam of our experiences.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".