Variability in Child Protection Medical Evaluations of Suspected Physical Abuse in Four European Countries: A Vignette Study
Bibliographic record
Abstract
When suspicions of physical abuse arise, children are referred for a child protection medical evaluation, which occurs in a variety of health settings by a variety of clinicians. This comparative vignette survey was performed among a cross‐section of medical professionals engaged in child protection in Sweden, Ireland, the UK and the Netherlands between April and July 2016. Three vignettes describing different probabilities of physical abuse were included. Components of child protection medical evaluations across the four countries were analysed. A total of 236 physicians responded (113 UK, 49 the Netherlands, 39 Sweden, 35 Ireland). Of these, 62 per cent were female (there were more females in the UK, Sweden and the Netherlands). More variation in practice than similarities was found. Similarities: experience level, confidence level and management approach (vignettes 1 and 3). Cross‐country differences: decision to investigate, adherence to national guidelines, experience versus specialism and subsequent management post‐assessment. These findings suggest the need for further exploration of practice between countries including specific and regular training, availability of support for non‐specialists and use of national and international clinical guidelines to promote best practice and reduce variation. More consideration of the human and financial cost to the healthcare system of unnecessary investigations and the length of hospital admission may be warranted. Key Practitioner Messages There is a lack of uniformity in the clinical management of physical abuse between these four European countries. Specific and regular training included in professional development plans and discussed at appraisal will promote clinician confidence in assessments. Expert support should be easily available to non‐specialists undertaking these assessments. National and local clinical guidelines are important tools in promoting best practice and reducing variation across and within countries.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".