860 Excellence exchanges to improve regional paediatric training
Bibliographic record
Abstract
Objectives The annual London School of Paediatrics(LSP) trainee survey, of on average 950 trainees, year on year shows variation in overall training placement satisfaction amongst the 31 Trusts. The 2022 LSP survey showed that 78% of trainees rated their placement as good/excellent, which leaves a quarter of trainees experiencing training which is average, below average and poor. To ensure a high standard of training is available to all trainees across Trusts and the variation in trainee placement satisfaction is minimised, Excellence Exchanges (‘EEs’) have been developed. The ‘EEs’ provide an opportunity for Trusts to showcase their ‘Excellence’ and share how they have resolved challenges faced in providing training. The Exchanges also ascertain using the LSP survey which areas of training to improve locally and develop solutions with support from the LSP and Deanery. Method The ‘EEs’ are widely advertised and individual Trusts voluntarily sign up to participate. The ‘EE’ is organised by the LSP Trainee Committee and supported by HEE/London Deanery (Head of School and TPDs) and LSP (College Tutors and Trust Reps). Integrated working between the Deanery, LSP and at the Trust level locally by the Trainees and Consultants is key to the execution and success of the ‘Exchange’. There is a preparation pack and the ‘Exchange’ follows a set structure with a Powerpoint to ensure the process is standardised and each ‘EE’ discusses; the LSP survey data, The Excellence (what and how maintained) and Improvements (what and plan). Exchange posters are completed and a local ‘EE’ champion supervises QI work and feedback. All excellence and learning from the Exchange is collated and shared on the LSP website and Bulletin. Results To date, six Trusts have participated in an ‘EE’ of which 2 were Tertiary centres and 4 District General Hospitals. Four exchanges occurred in person and 2 virtually. Three further Trusts have been scheduled. The feedback has been overwhelmingly positive. One college tutor commented ‘such a buzz and great to have your insights. Work afoot to start our action plan’. Another College Tutor commented on ‘the relaxed, friendly and non-threatening nature of the Exchange’. Conclusion The ‘Excellence Exchanges’ are a welcomed initiative by trainees and trainers to ensure high quality training is provided and maintained in Trusts across the LSP. The Exchanges are a structured and non-judgemental way for shared learning and improvement work to take place locally with support from the Deanery.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.020 |
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".