Care maps for children with medical complexity
Bibliographic record
Abstract
AIM: Children with medical complexity require multiple providers and services to keep them well and at home. A care map is a patient/family-created diagram that pictorially maps out this complex web of services. This study explored what care maps mean for families and healthcare providers to inform potential for clinical use. METHOD: Parents (n=15) created care maps (hand drawn n=10 and computer-generated n=5) and participated in semi-structured interviews about the process of developing care maps and their perceived impact. Healthcare providers (n=30) reviewed the parent-created care maps and participated in semi-structured interviews. Data were analysed for themes and emerging theory using a grounded theory analytical approach. RESULTS: Data analysis revealed 13 overarching themes that were further categorized into three domains: features (characteristics of care maps), functions (what care maps do), and emerging outcomes (benefits of care map use). These domains further informed a definition and a theoretical model of how care maps work. INTERPRETATION: Our findings suggest that care maps may be a way of supporting patient- and family-centred care by graphically identifying and integrating experiences of the family as well as priorities for moving forward. WHAT THIS PAPER ADDS: Care maps were endorsed as a useful tool by families and providers. They help healthcare providers better understand parental priorities for care. Parents can create care maps to demonstrate the complex burden of care. They are a unique visual way to incorporate narrative medicine into practice.
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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 | 0.001 | 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".