Going to work sick: A scoping review of illness presenteeism among physicians and medical trainees
Bibliographic record
Abstract
BACKGROUND: Illness presenteeism (IP)-characterized by individuals working despite being sick-is a prevalent and complex phenomenon among physicians and trainees amidst competing priorities within medicine. The COVID-19 pandemic and growing attention to physician and trainee well-being have sparked renewed interest in IP. We conducted a scoping review to explore what is known about IP: more specifically, how IP is perceived, what approaches have been used to study the phenomenon and how it might have changed through the COVID-19 pandemic. METHOD: The Arksey and O'Malley scoping review framework was used to systematically select and summarize the literature. Searches were conducted across four databases: Medline, Embase, PsycInfo and Web of Science. Quantitative and thematic analyses were conducted. RESULTS: Of 4277 articles screened, 45 were included. Of these, four were published after the onset of the COVID-19 pandemic. All studies framed IP as problematic for physicians, patients and health care systems. Dominant sociocultural drivers of IP included obligations towards patients and colleagues and avoiding the stigma of appearing vulnerable or even temporarily weak. Structural factors included heavy workload, poor access to health services and lack of sick leave policies for physicians. The pandemic does not appear to have affected IP-related causes or behaviours. Proposed solutions included both educational interventions and policy-driven changes. CONCLUSIONS: Despite being viewed in the literature as largely negative, IP remains highly prevalent among physicians and trainees. Our review highlights that IP among physicians is fraught with tensions: while IP seemingly contradicts certain priorities such as physician wellbeing, IP may be justified by fulfilling obligations to patients and colleagues. Future work should examine IP through diverse theoretical lenses to further elucidate its complexities and inform nuanced individual and systems-level interventions to minimize the negative consequences of IP.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".