Resident Supervision and Patient Care: A Comparative Time Study in a Community‐Academic Versus a Community Emergency Department
Bibliographic record
Abstract
OBJECTIVE: The objective was to compare attending emergency physician (EP) time spent on direct and indirect patient care activities in emergency departments (EDs) with and without emergency medicine (EM) residents. METHODS: We performed an observational, time-motion study on 25 EPs who worked in a community-academic ED and a nonacademic community ED. Two observations of each EP were performed at each site. Average time spent per 240-minute observation on main-category activities are illustrated in percentages. We report descriptive statistics (median and interquartile ranges) for the number of minutes EPs spent per subcategory activity, in total and per patient. We performed a Wilcoxon two-sample test to assess differences between time spent across two EDs. RESULTS: The 25 observed EPs executed 34,358 tasks in the two EDs. At the community-academic ED, EPs spent 14.2% of their time supervising EM residents. Supervision activities included data presentation, medical decision making, and treatment. The time spent on supervision was offset by a decrease in time spent by EPs on indirect patient care (specifically communication and electronic health record work) at the community academic ED compared to the nonacademic community ED. There was no statistical difference with respect to direct patient care time expenditure between the two EDs. There was a nonstatistically significant difference in attending patient load between sites. CONCLUSIONS: EPs in our study spent 14.2% of their time (8.5 minutes/hour) supervising residents. The time spent supervising residents was largely offset by time savings related to indirect patient care activities rather than compromising direct patient care.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".