Do you come to work with a respiratory tract infection?
Bibliographic record
Abstract
In caring for their patients, physicians strive to uphold the fundamental principle of medicine: primum non nocere – first do no harm. However, previous studies have reported that more than 80% of physicians come to work when they are ill.1–4 Numerous infections can be transmitted nosocomially, with some of the most common being the respiratory tract infections (RTIs).5 6 To explore this phenomenon, we created and sent three versions of an online survey to third year medical students, internal medicine and surgical residents, and staff physicians from the University of Toronto between June and August 2006. The questionnaire explored the frequency of working with an RTI and the factors that influenced this behaviour. The response rates for medical students, residents and staff physicians were 149/202 (73.8%), 317/650 (48.9%) and 202/350 (57.7%), respectively. The vast majority of respondents were ill for 1–2 days or more. Linear regression showed that when compared with residents, staff physicians reported an average of 0.9 fewer days with an RTI (p = 0.001) and students …
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".