Bibliographic record
Abstract
Summarizing our reflections on the past eight weeks is a daunting task. The opportunity to provide leadership for a hospital that has met the challenge of caring for clients with SARS (severe acute respiratory syndrome) has been unique. It has given us new understanding of system-wide and organizational processes, crisis management, our staff, the true meaning of organizational culture and teamwork and our own capacity to sustain leadership in the face of uncertainty. The SARS outbreak unleashed an unprecedented demand for leadership, courage and resilience among healthcare providers. The discovery of a new, highly contagious illness, with an almost 20% mortality rate, thrust the Ontario healthcare system into an emergency unparallelled in recent times. Although our experience and that of other healthcare administrators is likely quite different from that of the frontline staff, it is rich with lessons learned that can be applied in the future. Indeed, our own personal involvement included home quarantine when one of our colleagues had been diagnosed as a suspected SARS case. It is difficult to explain the impact of these events to others who have not been as closely involved with the Toronto SARS crisis. When we shared our observation that this would change healthcare forever, we were often asked why. Between us, we have about 54 years of healthcare experience, yet neither of us has ever before witnessed such an enduring test of leadership. Creating an air of calm, competence, trust, transparency and confidence was probably the single most important element of managing this event. We have shared the stress and emotional upset of conveying messages that we knew would raise great anxiety. There is no more difficult task than to share the news that members of your own staff have been A SARS Postcard – Reflections of Two Healthcare Leaders
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.025 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".