Provision of care for children with medical complexity in tertiary hospitals in England: qualitative interviews with health professionals
Bibliographic record
Abstract
BACKGROUND: Due to medical and technological advancements, children with medical complexity are a growing population. Although previous research has identified models of care and experiences when caring for this population, the majority are the USA or Canadian based. Therefore, the aim was to identify models of care for children with medical complexity and barriers and facilitators to delivering high-quality care for this population from a 'free at point of care' national health service. METHOD: Qualitative semistructured interviews were conducted with hospital clinicians across England and analysed using a thematic framework approach. RESULTS: Thirty-seven clinicians from 11 hospital sites were interviewed. In 6 of the hospital sites, there were 14 services identified. Majority of services had a variety of components, some shared and some unique to the individual service. Clinicians faced barriers and facilitators when caring for this population as demonstrated across five categories. CONCLUSIONS: There is limited guidance and evidence on the most effective and efficient models for providing care for this population. It is not possible to determine what a service should look like as there is no consensus on the most appropriate model of care as shown in this study. Due to their complex needs, this population require coordination to ensure high standards of care. However, this was not always possible as clinicians faced barriers such as time constraints, silo thinking and a lack of available housing.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".