Development of an institutional “good practices” policy for resident and attending-physician on-call responsibilities
Bibliographic record
Abstract
On-call coverage by resident physicians is common in academic hospitals, but the interaction between residents and supervising attending physicians varies. Responsibilities are often not clearly defined, which contributes to unclear expectations on the part of both. We developed an institutional “on-call responsibilities” guideline for both residents and attending physicians using a nominal group technique to gain consensus with multiple institutional stakeholders. Three focus groups engaged 31 clinical stakeholders in the development of concise guidelines that include 12 resident responsibilities and 12 attending-physician responsibilities that can be implemented while on-call. Using the nominal group technique allowed us to engage a large number of stakeholders and generate a robust guideline that could be easily operationalized to create a consistent expectation of responsibilities while on call, promote patient safety. It can also potentially reduce resident burnout. This quality-improvement project generated a list of concrete responsibilities that can be used in other centres and provides a robust approach to developing similar policies in other clinical contexts.
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 | 0.179 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".