Paediatric safeguarding simulation (PaSS) training: a novel approach to teaching child protection
Bibliographic record
Abstract
Child safeguarding is the responsibility of all healthcare professionals and in the UK, ‘Level 3 Safeguarding Children’ is a national requirement for clinical staff working with children, young people, their parents or carers.1 These professionals have a key role in identifying, assessing and reporting safeguarding concerns. This report describes the development and delivery of a new simulation programme, within a UK District General Hospital, to help increase staff confidence in managing child safeguarding in the clinical environment. Serious case reviews following safeguarding incidents in the UK have demonstrated that opportunities are often missed by front-line healthcare professionals during routine clinical encounters.2 Similar concerns have been raised in other countries, including the USA, Canada and Australia.3–5 Safeguarding concerns may arise in a number of healthcare settings: a child may present to hospital or their general practitioner with injuries or a medical emergency, during routine appointments, or during an encounter with a family member. It is important that healthcare professionals are trained in recognising and confidently managing these unexpected safeguarding presentations. In the UK, safeguarding is currently largely taught in an e-learning format with higher level training involving more face-to-face time in lecture/seminar sessions. These methods are useful for teaching the knowledge required, but are not as well suited to the affective elements and communication skills which are essential to effectively manage safeguarding cases. There is some evidence that simulation …
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".