Construct validity and reliability of the Handover Evaluation Scale
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To examine the psychometric properties of the Handover Evaluation Scale using exploratory and confirmatory factor analysis. BACKGROUND: Handover is a fundamental component of clinical practice and is essential to ensure safe patient care. Research indicates a number of problems with this process, with high variability in the type of information provided. Despite the reported deficits with handover practices internationally, guidelines and standardised tools for its conduct and evaluation are scarce. Further work is required to develop an instrument that measures the effectiveness of handover in a valid and reliable way. DESIGN: Secondary analysis of data collected between 2006-2008 from nurses working on 24 wards across a large Australian healthcare service. METHODS: A sample of 299 nurses completed the survey that included 20 self-report items which evaluated the effectiveness of handover. Data were analysed using exploratory factor analysis and confirmatory factor analysis supported by structural equation modelling. RESULTS: Analyses resulted in a 14-item Handover Evaluation Scale with three subscales: (1) quality of information (six items), (2) interaction and support (five items) and (3) efficiency (three items). A fourth subscale, patient involvement (three items), was removed from the scale as it was not a good measure of handover. CONCLUSIONS: The scale is a self-report, valid and reliable measure of the handover process. It provides a useful tool for monitoring and evaluating handover processes in health organisations, and it is recommended for use and further development. RELEVANCE TO CLINICAL PRACTICE: Monitoring handover is an important quality assurance process that is required to meet healthcare standards. This reliable and valid scale can be used in practice to monitor the quality of handover and provide information that can form the basis of education and training packages and guidelines to improve handover policies and processes.
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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 | 0.002 | 0.005 |
| 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".