Staff and resident perceptions on the introduction of a team based multi-specialty resident night shift system
Bibliographic record
Abstract
OBJECTIVES: To determine the perceptions of staff and resident physicians on the impact of implementation of a new team based multi-specialty resident night shift system. METHODS: An electronic survey was distributed anonymously to all resident physicians in the Core Internal Medicine residency program at the University of Alberta. A similar survey was distributed to staff physicians in the 4 specialties impacted by this new system: hematology, respirology, nephrology and gastroenterology. RESULTS: 74 physicians completed the survey. A majority of respondents (67%) indicated the new system was a positive change. Most shared it was better than traditional 1 in 4 call (65%), with resident physicians appreciating the team based nature of the system (65%), and just more than half of residents (55%) indicating this system improved their overall wellness. Most respondents (78%) did not feel the additional handover required had a negative impact. Respondents indicated daytime teaching and feedback improved as a result of this system (52%) with most others indicating it had no impact, although overnight feedback remained a challenge. CONCLUSION: The implementation of this new team based system was well accepted by both staff and resident physicians across a number of domains. Future study is required to determine its impact on access and quality of care.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".