Physician burnout and the risk factors associated
Bibliographic record
Abstract
Introduction: The practice of medicine can be incredibly rewarding, meaningful, and fulfilling to a physician, however it can be demanding and stressful. This paper set out to answer two main questions 1) what is the definition of ‘burnout’? and 2) What are the risk factors associated with physician ‘burnout’? Methods: A literature review was conducted to address the research questions. The PersonEnvironment-Occupation (PEO) Model was employed to structure the review of the literature with the main causes of burnout being highlighted in each of the person, environment and occupation domains of the model. Multiple databases were used in the collection of literature. Main Findings: It was evident that the current definition of burnout in the literature no longer reflects the needs of physicians and the healthcare field thus a reconceptualised definition is warranted. The organizational factors associated with physician burnout were disruptive behaviours, organizational climate, job satisfaction, organizational commitment and physician engagement. The main personal factors associated with physician burnout were medical training, work-life balance, sex and gender, personality traits and self-care. Lastly, the main environmental factors associated with physician burnout were autonomy, cultural shifts in medicine, perceptions of medicine in society and advances in medical technology. After assessing the definition and risk factors, it became clear that the issue of physician burnout requires an intersectional approach to fully understand physician needs and challenges. Conclusions: This paper highlighted many recommendations and considerations to advance burnout research and to increase the health and well-being of physicians including employing an intersectional approach as a starting point for fully understanding and preventing physician burnout.
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 | 0.001 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".