Family‐provider consensus outcomes for children with medical complexity
Bibliographic record
Abstract
AIM: To describe the process of obtaining consensus of outcome priorities between families of children with medical complexity (CMC) and their healthcare providers (HCPs) for the purpose of evaluating changes to service delivery. METHOD: The consensus of outcomes involved surveying families of CMC and HCPs and an in-person consensus meeting. Priorities were obtained from the survey using a stratified ranking approach ensuring equal representation among unequally sized subgroups. An in-person meeting was held using the survey results to inform Delphi voting. RESULTS: Families of CMC (n=40) and HCPs (n=74) responded to the survey. Consensus generated three main target areas (child health, family health, experience of care) covered by 15 specific outcomes needed to evaluate care. Differences between family and HCP perceptions of importance were found for child self-care, play, social skills, and recreation as well as emotional health (for both parent and child) outcomes. INTERPRETATION: Families of CMC and HCPs identified common priorities for outcome evaluation of CMC initiatives. Outcomes that differ in importance between families of CMC and HCPs should be studied further. WHAT THIS PAPER ADDS: Families of children with medical complexity and their providers can reach consensus on important outcomes. Stratifying subgroups ensures diverse representation, which is important to outcome prioritization.
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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 | 0.073 | 0.207 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".