Measure what matters: considerations for outcome measurement of care coordination for children with neurodevelopmental disabilities and medical complexity
Bibliographic record
Abstract
Introduction: Care Coordination (CC) is a significant intervention to enhance family's capacity in caring for children with neurodevelopmental disability and medical complexity (NDD-MC). CC assists with integration of medical and behavioral care and services, partnerships with medical and community-based supports, and access to medical, behavioral, and educational supports and services. Although there is some consensus on the principles that characterize optimal CC for children with NDD-MC, challenges remain in measuring and quantifying the impacts of CC related to these principles. Two key challenges include: (1) identification of measures that capture CC impacts from the medical system, care provider, and family perspectives; and (2) recognition of the important community context outside of a hospital or clinical setting. Methods: This study used a multilevel model variant of the triangulation mixed methods design to assess the impact of a CC project implemented in Alberta, Canada, on family quality of life, resource use, and care integration at the broader environmental and household levels. At the broader environmental level, we used linked administrative data. At the household level we used quantitative pre-post survey datasets, and aggregate findings from qualitative interviews to measure group-level impacts and an embedded multiple-case design to draw comparisons, capture the nuances of children with NDD-MC and their families, and expand on factors driving the high variability in outcome measures. Three theoretical propositions formed the basis of the analytical strategy for our case study evidence to explore factors affecting the high variability in outcome measures. Discussion: This study expanded on the factors used to measure the outcomes of CC and adds to our understanding of how CC as an intervention impacts resource use, quality of life, and care integration of children with NDD-MC and their families. Given the heterogeneous nature of this population, evaluation studies that account for the variable and multi-level impacts of CC interventions are critical to inform practice, implementation, and policy of CC for children with NDD-MC.
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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 | 0.378 | 0.544 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 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".