Development of the Pediatric Social Risk Instrument Using a Structured Panel Approach
Bibliographic record
Abstract
BACKGROUND: Social determinants of health impact child illness. Currently, no instrument exists to identify social need during hospital admission. METHODS: Using the UCLA (University of California Los Angeles)-RAND appropriateness method, consensus was reached for an instrument to identify social need in hospitalized children. A panel of 11 experts reviewed candidate indicators through 3 rounds to reach consensus. The instrument then underwent usability testing. RESULTS: Three hundred and forty-seven indicators from the literature were sorted into 18 social risk themes. After 3 rounds, consensus was reached on 82 indicators. Six additional social risk themes were recommended by the panel, resulting in consensus for 18 additional indicators. Final refinement resulted in an instrument containing 86 indicators representing 11 social risk themes. Usability testing identified that the tool was well received by families. Final feedback was incorporated into a post-usability instrument. CONCLUSIONS: Using the UCLA-RAND appropriateness method, a new pediatric social risk instrument was created to identify social need for hospitalized children.
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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 | 0.059 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".