Reduced time spent with patients and decreased satisfaction in work during COVID-19 pandemic
Bibliographic record
Abstract
Background: The COVID-19 pandemic disrupted the healthcare system, affecting physician wellbeing. The consequences of reduced time spent with patients at bedside during the pandemic has not been investigated. The objectives of this study include assessing time spent with patients, physician wellbeing and patient satisfaction before and during the pandemic. Methods: A total of 182 internal medicine physicians used Hill-Rom tracking devices to measure time spent at bedside while on the teaching hospital medicine service between September 2019 and November 2020 at Cleveland Clinic in Cleveland, Ohio. Time spent before and after March 2020, the outbreak of the pandemic were compared. Physicians' wellbeing was evaluated before and during the pandemic using the Accreditation Council for Graduate Medical Education survey. Patients' satisfaction was assessed via the Hospital Consumer Assessment of Healthcare Providers and Systems questionnaire and correlated to bedside time. Results: From 88,661 time records collected during the 65-week study, 44,710 (50.43%) met the quality standards and were included in the analysis. The average time spent at bedside per patient before and during the pandemic was 12.12 and 7.85 minutes, respectively. Time decreased by 3.33 minutes for interns, 6.10 minutes for residents, and 2.70 minutes for staff. The pandemic correlated with physicians' decreased vitality and meaning in work. Patients' satisfaction did not correlate with bedside time. Conclusion: Internal medicine physicians spent less time with patients during the pandemic and had worsened vitality and satisfaction with work. Physicians' time spent at bedside did not correlate with patients' satisfaction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".