What about physician wellness? Impact of a quality improvement intervention
Bibliographic record
Abstract
BACKGROUND: Emergency department (ED) consultations with general internal medicine (GIM) are required when patients need admission, assistance with safe disposition or evaluation and management of complex or acute medical needs. GIM physicians often balance responsibilities between hospital wards and the ED, which can lead to delayed ED consultations, difficulty balancing workload and potential burn-out. To address this issue, a quality improvement (QI) initiative was trialled, establishing a dedicated GIM ED consultation service to manage these duties independently. This study aimed to evaluate the impact of this intervention on physician wellness. METHODS: A pre-post design was used, with two questionnaires adapted from the validated Mini Z version 2.0 (Zero Burnout Program) Worklife measure for clinicians. These were distributed via Google Forms to collect feedback from participating GIM physicians before and after the intervention. Data were analysed using descriptive statistics and the Mini Z outcome measurement scale. RESULTS: 13 physicians completed the surveys. Applying the Mini Z scale, the GIM ED consultation service had no impact on physician well-being or burn-out. There was a minor increase in satisfaction (1 point) and stress levels (2 points), and the working environment worsened slightly (1 point). Comparing preintervention and postintervention survey responses, job satisfaction improved (36%), while reports of 'burn-out' (23%) and 'beginning to burn out' (8%) decreased. Postintervention, physicians reported decreased time for documentation (23%), a perception of a more chaotic work environment (23%) and an increase in work encroaching on personal time (15%) when on the ED consultation service. Additionally, there was a 23% reduction in the likelihood of needing to reduce clinical teaching unit service weeks. CONCLUSION: When conducting QI initiatives, consider measuring the wellness of physicians and other healthcare providers. Proactively integrating wellness strategies into interventions requires further exploration which may enhance participant experience and initiative sustainability.
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 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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".