Handover among multidisciplines in obstetrics: a mixed methods study of content and communication process
Bibliographic record
Abstract
Background: Communication failures during handover has long been noted as a threat to patient safety.1 Breakdown in communication among health professionals is reported to account for up to 85% of hospital sentinel events.1 Lack of formal structure and training in handover methods 53 as well as modes of communication including nonverbal behaviors 59 have been reported to affect the quality of information exchange in handovers. 59 The objective of this mixed methods study is to conduct a detailed analysis of handover content among physicians and among nurses on a Birthing Unit and to examine communication processes within these groups in order to identify gaps in the process. This may provide a basis for future development of standardized approaches and training efforts. Methods: A convergent, parallel mixed methodology was used. The sample in this study comprises the nurses and medical obstetrical team in a hospital Birthing Unit in Hamilton Ontario. Phase one of the study involved initial observations of handover, performing a Delphi to gain consensus on handover content and creating a handover assessment tool. Phase two involved reliability testing of the tool and in phase three, twenty five paired nurse to nurse compared to medical handovers were video recorded, scored and correlated to participant questionnaires. Results: Gaps in handover content were identified; the nurses achieved a mean score of 10.24 items compared to physicians mean score of 9.02 (correlation 0.582, p <0.01). This showed statistically significant differences in the items mentioned among the two groups iii (t = 13.2, p < 0.001). Nonverbal behaviors noted during handover observations revealed inconsistencies in conveying information, within and between the two groups. The findings of this study contribute to a better understanding of handover gaps, has implications to other health team practices and highlights the need for standardized processes, training and policy development.
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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 | 0.034 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".