Validation of the Finnish version of the food allergy quality of life questionnaire–parent form (<scp>F‐FAQLQ‐PF</scp>)
Bibliographic record
Abstract
AIM: Specific questionnaires about food allergy and health-related quality of life (HRQoL) have been developed and validated in several languages, but not Finnish. We aimed to validate the Finnish Food Allergy Quality of Life Questionnaire-Parent Form (FAQLQ-PF) for children under age 13 years. METHODS: The original FAQLQ-PF and Food Allergy Independent Measure (FAIM) were translated into Finnish per World Health Organisation guidelines and tested by 72 parents of children under age 13 years with suspected severe peanut or tree nut allergy who were recruited at Allergy Centre in Tampere University Hospital in Tampere, Finland. We calculated the construct validity of the questionnaire by calculating the coefficients between the two measures and used Cronbach's alpha to establish the internal consistency. RESULTS: The FAQLQ-PF showed strong correlations with domain structure and internal consistency, based on Spearman's correlations (rho) for the HRQoL questions, FAIM questions and FAIM mean values. The total questionnaire score correlated significantly with the mean FAIM (rho = 0.95, p < 0.001) and individual FAIM questions. The FAQLQ-PF and domains had good or excellent internal consistency (Cronbach's alpha >0.70). CONCLUSION: The Finnish FAQLQ-PF demonstrated good construct validity and excellent internal consistency for measuring food allergy HRQoL in children under age 13 years.
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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 | 0.013 | 0.018 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".