Resident physicians’ perceptions of COVID-19 risk
Bibliographic record
Abstract
BACKGROUND: Resident physicians provide front-line care to coronavirus disease 2019 (COVID-19) patients, but little is known about how they perceive the risk to their own health or how this is affected by the increasing role of social media in disseminating information. This study aims to determine resident physicians’ perceptions of personal COVID-19 risk during the first COVID wave and compare risk perceptions between low–average and high social media users. METHODS: We conducted a cross-sectional survey at the University of Toronto in May 2020 among resident physicians in internal medicine, emergency medicine, critical care, and anaesthesia. Participants were considered high social media users if above the median for daily social media use and low-average users if at or below the median. The primary outcome was perceived risk of hospitalization with COVID-19 within 6 months. RESULTS: A total of 98 resident physicians reported a median of 1–2 hours daily on social media, and 55.7% endorsed social media as a very or the most common source of information on COVID-19. The median overall perceived risk of hospitalization was 10% (inter-quartile ratio [IQR] 5–25)—7.5% for low–average social media users and 17.5% for high social media users ( p = 0.10). CONCLUSIONS: Resident physicians have an elevated perception of COVID-19 risk, including a perceived risk of hospitalization 250 times greater than the local population risk. Although social media are an important source of information on COVID-19, risk perception did not significantly differ between high and low–average social media users.
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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 | 0.001 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".