Improving trainee psychiatrist's handover: standard setting and audit
Bibliographic record
Abstract
Purpose – The 2006 Post Graduate Medical Education Trust Board (PMETB) trainees' survey indicated inadequacies in handover procedures amongst medical and psychiatry trainees nationwide; and in 2007 a local psychiatry trainees' survey found inadequate handover procedures. The purpose of this paper is to show how to improve handover practice through standard setting and sequential audit. Design/methodology/approach – A Trust wide Standard Operating Procedure (SOP) for handover was developed. Trainees were audited on perception of handover experiences (2008, 2009 and 2010). Findings – The audit revealed that the SOP was not consistently followed. Handing over “active problems” (AP) was perceived to occur frequently in 2008 (93.75 per cent), improved in 2009 (100 per cent for AP, 98 per cent for “problems which may arise”, (PA)); however deteriorated in 2010 (93 per cent for AP, 69 per cent for PA). Trainee satisfaction rates with handover improved each year (57 per cent in 2008, 75 per cent in 2009, 87 per cent in 2010, X2=3.7, df=2, p=0.16). Practical implications – SOP development, subsequent audits and sharing of results improved handover practice. This has implications for training and patient safety. This project demonstrates a method of improving handover practices in a large mental health trust. Originality/value – The work conducted is of interest to those working in psychiatry, not only from an education and training perspective, but also for clinical practice, in the UK as well as internationally.
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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 | 0.089 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".