The relationship between resident burnout and safety-related and acceptability-related quality of healthcare: a systematic literature review
Bibliographic record
Abstract
BACKGROUND: There has been increasing interest in examining the relationship between physician wellbeing and quality of patient care. However, few reviews have specifically focused on resident burnout and quality of patient care. The purpose of this systematic literature review of the current scientific literature is to address the question, "How does resident burnout affect the quality of healthcare related to the dimensions of acceptability and safety?" METHODS: This systematic literature review uses a multi-step screening process of publicly available peer-reviewed studies from five electronic databases: (1) Medline Current, (2) Medline In-process, (3) PsycINFO, (4) Embase, and (5) Web of Science. RESULTS: The electronic literature search resulted in the identification of 4638 unique citations. Of these, 10 articles were included in the review. Studies were assessed for risk of bias. Of the 10 studies that met the inclusion criteria, eight were conducted in the US, one in The Netherlands, and one in Mexico. Eight of the 10 studies focused on patient safety. The results of these included studies suggest there is moderate evidence that burnout is associated with patient safety (i.e., resident self-perceived medical errors and sub-optimal care). There is less evidence that specific dimensions of burnout are related to acceptability (i.e., quality of care, communication with patients). CONCLUSIONS: The results of this systematic literature review suggest a relationship between patient safety and burnout. These results potentially have important implications for the medical training milieu because residents are still in training and at the same time are asked to teach students. The results also indicate a need for more evidence-based interventions that support continued research examining quality of care measures, especially as they relate to acceptability.
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 | 0.026 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".