Care maps and care plans for children with medical complexity
Bibliographic record
Abstract
INTRODUCTION: The support of families in the care of children with medical complexity (CMC) requires the integration of health care providers' (HCPs') medical knowledge and family experience. Care plans largely represent HCP information, and care maps demonstrate the family experience. Understanding the intersection between a care plan and a care map is critical, as it may provide solutions to the widely recognized tension between HCP-directed care and patient- and family-centered care (PFCC). METHOD: This study used qualitative methods to explore the experience and usefulness of care maps. Parents of CMC who already had a care plan, created care maps (n = 15). Subsequent interviews with parents (n = 15) and HCPs (n = 30) of CMC regarding both care maps and care plans were conducted and analyzed using thematic analysis. RESULTS: Data analysis exploring the relationship and utility of care plans and care maps revealed six primary themes related to using care plans and care maps that were grouped into two primary categories: (a) utility of care plans and maps; and (b) intersection of care plans and care maps. DISCUSSION: Care plans and care maps were identified as valuable complementary documents. Their integration offers context about family experience and respects the parents' experiential wisdom in a standard patient care document, thus promoting improved understanding and integration of the family experience into care decision making.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".