First stage international validation of the pediatric family needs questionnaire (FNQ-P)
Bibliographic record
Abstract
Objectives 1. Evaluate reliability and construct validity of the newly-developed Family Needs Questionnaire – Pediatric (FNQ-P), a 40-item measure assessing the extent to which family’s needs are met after a child has an acquired brain injury (ABI). 2. Explore the impact of selected child characteristics on FNQ-P scores.Research Design: Measurement study Methods: Parents/caregivers of children with ABI (2–18 years) were recruited across four sites (Canada, Sweden, Lithuania, Australia) for FNQ-P test–retest evaluation. These sites plus a fifth (United Kingdom) completed construct validity evaluation with the Family Burden of Injury Inventory and Strengths and Difficulties Questionnaire. Associations between FNQ-P score and age, injury severity, time post-injury and site were evaluated via stepwise regression.Results FNQ-P mean scores (n=61) were 64.1% (SD 22.3) and 58.8% (SD 22.6) on test and retest, respectively. Test–retest reliability was good overall (ICC=0.78, 95% CI 0.65–0.86). There was a weak association between FNQ-P and FBII (r=−0.23, P=0.049, n=71), but no association between FNQ-P and SDQ scores (maximum r=0.16, P>0.15). None of the variables studied predicted FNQ-P scores.Conclusion The FNQ-P demonstrated good test–retest reliability. Further validity assessment is recommended. Lack of relationship between FNQ-P and variables studied suggests independence of family needs.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".