Qualitative study exploring factors influencing escalation of care of deteriorating children in a children’s hospital
Bibliographic record
Abstract
BACKGROUND: System-level interventions including rapid response teams and paediatric early warning scores have been designed to support escalation of care and prevent severe adverse events in hospital wards. Barriers and facilitators to escalation of care have been rarely explored in paediatric settings. AIM: This study explores the experiences of parents and healthcare professionals of in-hospital paediatric clinical deterioration events to identify factors associated with escalation of care. METHODS: Across 2 hospital sites, 6 focus groups with 32 participants were conducted with parents (n=9) and healthcare professionals (n=23) who had cared for or witnessed a clinical deterioration event of a child. Transcripts of audio recording were analysed for emergent themes using a constant comparative approach. FINDINGS: Four themes and 19 subthemes were identified: (1) impact of staff competencies and skills, including personal judgement of clinical efficacy (self-efficacy), differences in staff training and their impact on perceived nursing credibility; (2) impact of relationships in care focusing on communication and teamwork; (3) processes identifying and responding to clinical deterioration, such as patient assessment practices, tools to support the identification of patients at risk and the role of the rapid response team; and (4) influences of organisational factors on escalation of care, such as staffing, patient pathways and continuity of care. CONCLUSIONS: Findings emphasise the considerable influence of social processes such as teamwork, communication, models of staff organisation and staff education. Further studies are needed to better understand how modification of these factors can be used to improve patient safety.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".