Patient handover between ambulance crew and healthcare professionals in Icelandic emergency departments: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Ambulance services play an important role in the healthcare system when it comes to handling accidents or acute illnesses outside of hospitals. At the time of patient handover from emergency medical technicians (EMTs) to the nurses and physicians in emergency departments (EDs), there is a risk that important information will be lost, the consequences of which may adversely affect patient well-being. The study aimed to describe healthcare professionals' experience of patient handovers between ambulance and ED staff and to identify factors that can affect patient handover quality. METHODS: The Vancouver School's phenomenological method was used. The participants were selected using purposive sampling from a group of Icelandic EMTs, nurses, and physicians who had experience in patient handovers. Semi-structured individual interviews were conducted and were supported by an interview guide. The participants included 17 EMTs, nurses, and physicians. The process of patient handover was described from the participants' perspectives, including examples of communication breakdown and best practices. RESULTS: Four main themes and nine subthemes were identified. In the theme of leadership, the participants expressed that it was unclear who was responsible for the patient and when during the process the responsibility was transferred between healthcare professionals. The theme of structured framework described the communication between healthcare professionals before patient's arrival at the ED, upon ED arrival, and a written patient report. The professional competencies theme covered the participants' descriptions of professional competences in relation to education and training and attitudes towards other healthcare professions and patients. The collaboration theme included the importance of effective teamwork and positive learning environment. CONCLUSIONS: A lack of structured communication procedures and ambiguity about patient responsibility in patient handovers from EMTs to ED healthcare professionals may compromise patient safety. Promoting accountability, mitigating the diffusion of responsibility, and implementing uniform practices may improve patient handover practices and establish a culture of integrated patient-centered 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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".