Equitable Care for Children With a Tracheostomy: Addressing Challenges and Seeking Systemic Solutions
Bibliographic record
Abstract
BACKGROUND: Children with medical complexity (CMC) often face significant barriers to accessing care, obtaining appropriate insurance coverage for medical devices, technology, supplies, home nursing and social services. These challenges, when viewed through the lens of social determinants of health, highlight concerns about healthcare inequity. These inequities can impact CMC by limiting access to follow-up appointments, leading to disproportionate use of emergency department services, restricting support services, reducing the quality of medical products and increasing the likelihood of adverse events. Addressing these concerns requires comprehensive policy changes at both state and federal levels. Achieving successful collaborations between states and federal agencies is particularly challenging and may take months or even years to accomplish. OBJECTIVES: Through an exploratory qualitative approach, this study facilitates a nuanced inquiry into the experiences and systemic challenges encountered by medical professionals and primary caregivers managing CMC who require a paediatric tracheostomy. METHODS: Qualitative interviews were conducted with 17 health professionals and primary caregivers residing in the United States. A thematic analysis was used to analyse the transcribed interview data. RESULTS: Using exploratory thematic analysis, we identified challenges and opportunities for improvement regarding (a) access to health insurance, (b) procurement of essential medical supplies, (c) logistical constraints and (d) identifying interim solutions. CONCLUSION: Building on our findings, we discuss how socioecological factors impact health and quality of life of CMC and families. Additionally, we address the growing gap in quality of care through a comprehensive approach that considers patient needs, regulatory frameworks and affordability. PATIENT OR PUBLIC CONTRIBUTION: Medical practitioners and healthcare professionals were actively involved in the development, production and implementation of the research project. These individuals were given the opportunity to review their statements and review the manuscript before publishing. While caregivers did not engage in member checking, each provided their consent before data collection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".