Perspectives of Pediatric Providers on Patients With Complex Chronic Conditions: A Mixed-Methods Sequential Explanatory Study
Bibliographic record
Abstract
BACKGROUND: Children with complex chronic conditions present unique challenges to the pediatric intensive care unit, including prolonged length of stay, complex medical regimens, and complicated family dynamics. OBJECTIVES: To examine perspectives of pediatric intensive care unit health care providers regarding pediatric patients with complex chronic conditions, and to explore potential opportunities to improve these patients' care. METHODS: A prospective mixed-methods sequential explanatory study was conducted in a tertiary medical-surgical pediatric intensive care unit using surveys performed with REDCap (Research Electronic Data Capture) followed by semistructured interviews. RESULTS: The survey response rate was 70.6% (77 of 109). Perspectives of health care providers did not vary with duration of work experience. Ten semistructured interviews were conducted. Eight overarching themes emerged from the interviews: (1) the desire for increased formal education specific to pediatric complex chronic care patients; (2) designation of a primary intensivist; (3) modifying delivery of care to include a discrete location for care provision; (4) establishing daily, short-term, and long-term goals; (5) monitoring and documenting care milestones; (6) strengthening patient and family communications with the health care team; (7) optimizing discharge coordination and planning; and (8) integrating families into care responsibilities. CONCLUSIONS: Pediatric intensive care unit health care providers' perspectives of pediatric patients with complex chronic conditions indicated opportunities to refine the care provided by establishing daily goals, coordinating discharge planning, and creating occasions for close communication between patients, families, and providers.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".