<scp>COVID</scp>‐19 in the pre‐pandemic period: a survey of the time commitment and perceptions of infectious diseases physicians in Australia and New Zealand
Bibliographic record
Abstract
BACKGROUND: Infectious diseases (ID) physicians perform a pivotal role in directing the response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). AIM: To assess the impact of SARS-CoV-2 on workload and the perceptions of ID physicians regarding the national response in Australia and New Zealand in the pre-pandemic. METHODS: A survey of ID physicians in Australia and New Zealand was undertaken from 3 to 10 March 2020. Respondents were asked to estimate time spent on SARS-CoV-2-related activities in February and report their agreement with statements on a 5-point Likert scale ranging from 'strongly agree' to 'strongly disagree'. We also asked about the intended use of investigational agents. RESULTS: There were 214 respondents (36% of 600 eligible participants). The median workload due to SARS-CoV-2-related activities was 34% of one full-time equivalent (interquartile range 18-68%). Less than a quarter (50, 23%) of respondents had experience managing cases, while 33% (70) had experience preparing during similar pandemics. Nevertheless, 88% (188/213) believed they were well informed when giving testing and management advice, and 45% (95/212) believed their national response was well coordinated. Additionally, 41% (88/214) were worried about becoming infected through occupational exposure. Over half (116, 54%) the respondents intended to use lopinavir/ritonavir in confirmed cases of COVID-19 with severe disease. CONCLUSIONS: ID physicians spent a large proportion of time on SARS-CoV-2-related activities. Increased staffing is required to avoid burnout. Importantly, ID physicians feel well informed when giving advice. A national body should be established to co-ordinate response. Treatment efficacy trials are needed to clarify the utility of unproven treatments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".