Residents' and attendings' perceptions of a night float system in an internal medicine program in Canada
Bibliographic record
Abstract
BACKGROUND: The Night Float system (NFS) is often used in residency training programs to meet work hour regulations. The purpose of this study was to examine resident and attendings' perceptions of the NFS on issues of resident learning, well-being, work, non-educational activities and the health care system (patient safety and quality of care, inter-professional teams, workload on attendings and costs of on-call coverage). METHODS: A survey questionnaire with closed and open-ended questions (26 residents and eight attendings in an Internal Medicine program), informal discussions with the program and moonlighting and financial data were collected. RESULTS AND DISCUSSION: The main findings included, (i) an overall congruency in opinions between resident and attendings across all mean comparisons, (ii) perceptions of improvement for most aspects of resident well-being (e.g. stress, fatigue) and work environment (e.g. supervision, support), (iii) a neutral effect on the resident learning environment, except resident opinions on an increase in opportunities for learning, (iv) perceptions of improved patient safety and quality of care despite worsened continuity of care, and (v) no increases in work-load on attendings or the health care system (cost-neutral call coverage). Patient safety, handovers and increased utilization of moonlighting opportunities need further exploration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".