Emergency physician attitudes towards illness verification (sick notes)
Bibliographic record
Abstract
INTRODUCTION: Emergency physicians frequently provide care for patients who are experiencing viral illnesses and may be asked to provide verification of the patient's illness (a sick note) for time missed from work. Exclusion from work can be a powerful public health measure during epidemics; both legislation and physician advice contribute to patients' decisions to recover at home. METHODS: We surveyed Canadian Association of Emergency Physicians members to determine what impacts sick notes have on patients and the system, the duration of time off work that physicians recommend, and what training and policies are in place to help providers. Descriptive statistics from the survey are reported. RESULTS: A total of 182 of 1524 physicians responded to the survey; 51.1% practice in Ontario. 76.4% of physicians write at least one sick note per day, with 4.2% writing 5 or more sick notes per day. Thirteen percentage of physicians charge for a sick note (mean cost $22.50). Patients advised to stay home for a median of 4 days with influenza and 2 days with gastroenteritis and upper respiratory tract infections. 82.8% of physicians believe that most of the time, patients can determine when to return to work. Advice varied widely between respondents. 61% of respondents were unfamiliar with sick leave legislation in their province and only 2% had received formal training about illness verification. CONCLUSIONS: Providing sick notes is a common practice of Canadian Emergency Physicians; return-to-work guidance is variable. Improved physician education about public health recommendations and provincial legislation may strengthen physician advice to patients.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".