31 Strengthening care for infants with medical complexity during the transition from the neonatal intensive care unit to the community
Bibliographic record
Abstract
Abstract Background With advances in technology and innovative medical treatments, infants who previously would have died in early infancy are living longer. These infants have significant needs in the medical system and pose challenges in coordinating care from multiple providers, especially after discharge from NICU. Objectives To engage neonatal-to-early-childhood care-transition stakeholders in implementing system-level change to champion the successful transition of infants with medical complexity into early childhood. We believe this engagement activity will spark action amongst stakeholders to adopt best practices leading to improvements in care for this vulnerable patient population. Design/Methods We explored four key care transition related questions: We organized journey mapping and focus groups with NICU alumni families whose children have medical complexity. We conducted a literature review to identify evidence-based interventions. We compiled this information to create evidence briefs that were presented to an interprofessional group of senior stakeholders using the deliberative dialogue approach. Following presentation of the evidence briefs surveys were administered to measure stakeholder intention to act on solutions presented. Results Twenty-five opportunities for improving transitions between hospital and community care teams were identified through engagement with families and project team members (graphic A). These opportunities focused on facilitating clinical navigation, navigating community services, and improving parental mental health. Forty-two stakeholders representing a children’s and women’s hospital, families, community care providers, and provincial bodies were engaged in this project. A rapid review of the literature was carried out using multiple databases searches to ensure comprehensiveness and flexible search strategies to ensure literature was found for all issues. This included a broader search of NICU discharges and nine targeted searches with medical complexity populations. Proposed solutions from stakeholders were further validated through comparisons with the literature and ultimately thirty-five papers were included in the evidence briefs. Specific care interventions were recommended to key healthcare decision makers, such as providing post-discharge care coordination in a specialized complex care clinic, implementing patient-oriented discharge summaries, and facilitating access to mental health resources. Using a seven point Likert scale the majority of stakeholders agreed that both the evidence briefs and deliberative dialogues were very successful in achieving their aim. Stakeholder intention to act on solutions presented in evidence briefs and deliberative dialogues were rated as very likely. Conclusion
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".