Toward an Understanding of Advance Care Planning in Children With Medical Complexity
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Children with medical complexity (CMC) often have multiple life-limiting conditions with no unifying diagnosis and an unclear prognosis and are at high risk for morbidity and mortality. Advance care planning (ACP) conversations need to be uniquely tailored to this population. Our primary objective for this study was to develop an in-depth understanding of the ACP experiences from the perspectives of both parents and health care providers (HCPs) of CMC. METHODS: We conducted 25 semistructured interviews with parents of CMC and HCPs of various disciplines from a tertiary pediatric hospital. Interview guide questions were focused on ACP, including understanding of the definition, positive and negative experiences, and suggestions for improvement. Interviews were conducted until thematic saturation was reached. Interviews were audio recorded, transcribed verbatim, coded, and analyzed using content analysis. RESULTS: Fourteen mothers and 11 HCPs participated in individual interviews. Interviews revealed 4 major themes and several associated subthemes (in parentheses): (1) holistic mind-set, (2) discussion content (beliefs and values, hopes and goals, and quality of life), (3) communication enhancers (partnerships in shared decision-making, supportive setting, early and ongoing conversations, consistent language and practice, family readiness, provider expertise in ACP discussions, and provider comfort in ACP discussions), and (4) the ACP definition. CONCLUSIONS: Family and HCP perspectives revealed a need for family-centered ACP for CMC and their families. Our results aided the development of a family-centered framework to enhance the delivery of ACP through a holistic mind-set, thoughtful discussion content, and promoting of conversation enhancers.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".