The Child-Focused Injury Risk Screening Tool (ChildFIRST) Demonstrates Greater Reliability When Using a Dichotomous Scale vs. a Seven-Point Likert Scale, and Is Preferred by Raters
Bibliographic record
Abstract
The Child-Focused Injury Risk Screening Tool (ChildFIRST) assesses movement competence in children and currently uses a dichotomous scoring scale, which, while simple and practical, may lack the precision needed for nuanced movement skill analysis. This study compared the inter- and intra-rater reliability of the ChildFIRST when scored using a dichotomous scale versus a seven-point Likert scale. Fourteen trained raters evaluated video recordings of eight children performing ten standardized movement tasks using both scales across two sessions. Reliability was assessed using intraclass correlation coefficients (ICCs). The dichotomous scale demonstrated moderate to excellent inter-rater reliability (ICC = 0.50–1.00) and good to excellent intra-rater reliability (ICC = 0.75–1.00). The seven-point scale showed similar inter-rater reliability but generally lower intra-rater reliability (ICC = 0.50–1.00). In addition, raters preferred the dichotomous scale in terms of practicality (91.6%), feasibility (75%), and overall usability (66.6%). These findings suggest that while both scales provide comparable inter-rater agreement, the dichotomous format offers greater consistency across repeated ratings and is more favorably received by users. The dichotomous scoring system is therefore recommended for continued use in field-based screening and future applications of the ChildFIRST.
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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 | 0.023 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".