Illness presenteeism among physicians and trainees: Study protocol of a scoping review
Bibliographic record
Abstract
BACKGROUND: Illness presenteeism (IP) is the phenomenon where individuals continue to work despite illness. While it has been a prevalent and longstanding issue in medicine, the recent onset of the COVID-19 pandemic and the growing movement to improve physician wellness brings renewed interest in this topic. However, there have been no comprehensive reviews on the state of literature of this topic. PURPOSE: The main aim of this scoping review is to explore what is known about presenteeism in physicians, residents, and medical students in order to map and summarize the literature, identify research gaps and inform future research. More specifically: How has illness presenteeism been defined, problematized or perceived? What methods and approaches have been used to study the phenomenon? Has the literature changed since the pandemic? METHOD: Using the Arksey and O'Malley framework several databases will be searched by an experienced librarian. Through an iterative process, inclusion and exclusion criteria will be developed and a data extraction form refined. Data will be analyzed using quantitative and qualitative content analyses. POTENTIAL IMPLICATIONS OF RESULTS: By summarizing the literature on IP, this study will provide a better understanding of the IP phenomena to inform future research and potentially have implications for physician wellness and public health.
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 | 0.087 | 0.085 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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".