Initial assessment of patient handoff in accredited general surgery residency programs in the United States and Canada: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Communication errors are considered one of the major causes of sentinel events. Our aim was to assess the process of patient handoff among junior surgical residents and to determine ways in which to improve the handoff process. METHODS: We conducted nationwide surveys that included all accredited general surgery residency programs in the United States and Canada. RESULTS: Of the 244 American and 17 Canadian accredited surgical residency programs contacted, 65 (27%) and 12 (71%), respectively, participated in the survey. Of the American and Canadian respondents, 66% and 69%, respectively, were from postgraduate year (PGY) 1, and 32% and 29%, respectively, were from PGY 2; 85 (77%) and 50 (96%), respectively, had not received any training about patient handoff before their surgical residency, and 27% and 64%, respectively, reported that the existing handoff system at their institutions did not adequately protect patient safety. Moreover, 29% of American respondents and 37% of Canadian respondents thought that the existing handoffs did not support continuity of patient care. Of the American residents, 67% and 6% reported receiving an incomplete handoff that resulted in minor and major patient harm, respectively. These results mirrored those from Canadian residents (63% minor and 7% major harm). The most frequent factor reported to improve the patient handoff process was standardization of the verbal handoff. CONCLUSION: Our survey results indicate that the current patient handoff system contributes to patient harm. More efforts are needed to establish standardized forms of verbal and written handoff to ensure patient safety and continuity of 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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".