18 The nurse practitioner in the care of children with medical complexity: A role optimization study
Bibliographic record
Abstract
Abstract Background Children with medical complexity (CMC) are characterized by complex chronic conditions, high healthcare utilization, functional limitations/technology dependence and a significant impact on their family. Nurse Practitioners (NP) are ideally suited to lead and support the complex needs of CMC. While NP-led care is increasingly used in Complex Care Programs across North America, the role of the NP in this setting has not been formally investigated or defined. Objectives To explore how NPs, health care providers (HCPs), and parents perceive and define the role of NP within Complex Care Programs and identify opportunities to enhance the effectiveness and efficiency of NP practice in caring for CMC. Design/Methods Utilizing a qualitative grounded theory design, this study employed theoretical sampling to recruit NPs, HCPs, and parents from Complex Care Programs in Ontario, to capture diverse participant experiences. Semi-structured interviews were conducted virtually and audio-recorded, transcribed verbatim, and independently coded by two researchers. An iterative analytic process was used to identify relationships between constructs, while staying grounded in the participants' voice. Adherence to quality standards ensured rigour, employing techniques such as member checking, thick description, and triangulation. Results Interviews were conducted with NPs (n=11), HCPs (n=24), and parents (n=15) from across 12 Complex Care Programs. A comprehensive theoretical framework for advancing NP practice in the care of CMC was developed (Figure 1). The framework demonstrates 5 key pillars of NP practice and identifies factors that impede and strengthen these practices. Key recommendations were generated to enhance NP capacity, role sustainability, unique skill sets, and supportive team structures. Conclusion This study offers insights into defining and optimizing NP practice within Complex Care Programs, proposing opportunities to advance NP-led care delivery, improve CMC and family experiences, and transform complex care settings nationally and beyond.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".