The relationship between physician burnout and quality of healthcare in terms of safety and acceptability: a systematic review
Bibliographic record
Abstract
OBJECTIVES: This study reviews the current state of the published peer-reviewed literature related to physician burnout and two quality of care dimensions. The purpose of this systematic literature review is to address the question, 'How does physician burnout affect the quality of healthcare related to the dimensions of acceptability and safety?' DESIGN: . SETTING: Physicians practicing in civilian settings. PARTICIPANTS: Practicing physicians who have completed training. PRIMARY AND SECONDARY OUTCOME MEASURES: Quality of healthcare related to acceptability (ie, patient satisfaction, physician communication and physician attitudes) and safety (ie, minimising risks or harm to patients). RESULTS: 4114 unique citations were identified. Of these, 12 articles were included in the review. Two studies were rated as having high risk of bias and 10 as having moderate risk. Four studies were conducted in North America, four in Europe, one in the Middle East and three in East Asia. Results of this systematic literature review suggest there is moderate evidence that burnout is associated with safety-related quality of care. Because of the variability in the way patient acceptability-related quality of care was measured and the inconsistency in study findings, the evidence supporting the relationship between burnout and patient acceptability-related quality of care is less strong. CONCLUSIONS: The focus on direct care-related quality highlights additional ways that physician burnout affects the healthcare system. These studies can help to inform decisions about how to improve patient care by addressing physician burnout. Continued work looking at the relationship between dimensions of acceptability-related quality of care measures and burnout is needed to advance the field.
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 | 0.027 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".