improving Pain mAnagement for childreN and young people attendeD by Ambulance (PANDA): protocol for a realist evaluation and consensus workshops.
Bibliographic record
Abstract
Background: Prehospital pain management in children and young people (CYP) is a top research priority in paediatric emergency medicine. Approximately 90,000 CYP under 18 years of age are transported to hospital by ambulance each year for conditions associated with acute pain in England. Approximately half of the children suffering from acute pain do not receive analgesics from paramedics, and approximately 60% do not experience a meaningful reduction in their pain severity. The aim of these studies is to explore the experiences of key stakeholders, refine our programme theory, and prioritise candidate intervention components to address the gaps in care. Methods: A realist evaluation using a multiple case study approach following the RAMESIS II guidance and consensus workshops using the modified nominal group technique will be performed. The realist evaluation will collect qualitative data (interviews, diaries, electronic messaging, arts-based materials) from CYP aged 4-17 years who have experienced acute pain and needed an ambulance in the previous 12 months, parents and carers, and ambulance clinicians. Recruitment will occur across England within three NHS ambulance services, two NHS children's emergency departments, and via social media. We will also collect routine quantitative clinical record data from two NHS ambulance services. We will use a realist logic of analysis to code the data into conceptual buckets, develop context-mechanism-outcome configurations, and refine the programme theory developed from a previous realist review. Two consensus workshops will be held: one for CYP aged 8-17 years, parents and carers, public representatives, and members of the Young Persons Advisory Group, and one for ambulance clinicians, quality leads, and educators. Workshop attendees will review the study findings and candidate intervention components, vote on their importance, and review voting results. High-priority intervention components will be considered for incorporation into an intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.110 | 0.094 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.086 | 0.016 |
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".