A Taster Of An Award-Winning Conflict Resolution Training Program For Pediatric Health Professionals
Bibliographic record
Abstract
In 2013, the Medical Mediation Foundation and the Evelina London Children’s Hospital initiated a project to explore the nature and impact of conflict across paediatrics. Interestingly, staff were initially reluctant to name disagreements as ‘conflict’, but widespread canvassing of experience yielded a working definition of conflict which has ‘the breakdown of trust and communication breakdown’ and "impact on the ability of staff to provide optimal care to the child" at its core. The project, based on published research with families and health professionals, provides training to staff in recognising and managing conflict and an independent mediation service available to families, patients and staff to help resolve conflict if it escalates. The Evelina Resolution Project has become a nationally recognised, award-winning training programme. Interactive, multi-disciplinary sessions (usually half days, 12-20 staff) are co-trained, combining the expertise of a senior consultant paediatrician and an experienced accredited mediator. Six month follow up of a cohort of 313 staff found that more than half had experienced a conflict with a parent or patient since doing the training and of these, 95% reported that the training had helped them to recognise the warning signs and 91% said it had helped de-escalate the conflict. Feedback from more than 1600 Evelina staff trained to date, provides consistently high ‘quality’ ratings (95% rated the training as excellent/very good), ‘relevance’ ratings (99% - very relevant/relevant) This workshop will offer a condensed version of the training and an opportunity for participants to practise and discuss the skills taught.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".