Bibliographic record
Abstract
ABSTRACTAims and ObjectivesThis paper reports on a study of clinical incidents related to the transfer of accountability and responsibility of patient care during clinical handover in three major health facilities in regional Australia. It aims to identify significant issues in the area of transferring accountability and responsibility.BackgroundAlthough clinical handover is widely acknowledged as the process which transfers accountability and responsibility, issues occur particularly when this transfer is incomplete, shared or when one clinician feels an ongoing sense of responsibility for the patient.DesignA thematic analysis of incidents related to clinical handover was conducted on data collected at three regional settings within Australia in order to identify issues which had occurred during this process.MethodsThe Incident Information Management System (IIMS) is a database that collects information about clinical incidents and near misses and relies on health staff to report them. The initial information retrieved from IIMs identified 3716 possible events for inclusion. A thematic analysis was undertaken of the data which identified transfer of responsibility and accountability as a key theme.ResultsThe data related to the transfer of responsibility and accountability came to prominence in the incident reports in three ways. These included; identifying omissions, issues with information exchange and refusal to accept responsibility of care.ConclusionsThis study demonstrates the need for a more systematic approach regarding communication between health professionals regarding the transferability and accountability of patient care.Relevance to Clinical PracticeClinical handover remains a contentious issue regarding patient care and safety. Health professionals may benefit from this review of incidents related to clinical handover and consider some of the recommendations to improve clinical practice.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".