Current tools measuring anxiety in parents of food‐allergic children are inadequate
Bibliographic record
Abstract
BACKGROUND: In the context of food allergy, excessive parental anxiety can be maladaptive and lead to unnecessary restriction of social activities. No validated tool exists to measure food allergy-associated anxiety (FAAA). This study sought to explore factors associated with parental FAAA, determine sensitivity and specificity of using generic state anxiety measure-State-Trait Anxiety Inventory (STAI) versus FAAA, and determine whether validated tools for generalized anxiety or food allergy-specific quality of life (QoL) could be used as surrogates for FAAA. METHODS: Canadian parents of food-allergic children completed an online survey. Without a validated tool for FAAA, a visual analogue scale was used to assess parent-reported FAAA. Multivariable linear regression was performed with FAAA as the outcome. Sensitivity and specificity analysis of state anxiety vs. FAAA, and factor analysis of state anxiety and QOL, was performed to determine whether these could be used as surrogates for FAAA. RESULTS: A total of 548 of 1244 parents (44.1%) completed the survey. Factors positively associated with FAAA included parental burden, risk perception, state anxiety, intolerance of uncertainty and perceived severity of child's food allergy; personal/family history of mental health was negatively associated. Sensitivity and specificity of state anxiety were 68.6% and 70.0%. Factor analysis revealed that state anxiety and QOL were correlated (r = 0.54, P < .001) but distinct constructs. CONCLUSION: Our study identified factors associated with FAAA, and determined that generic anxiety and QOL tools do not accurately categorize parents with self-reported high FAAA. Future research will develop a validated screening tool to help allergists identify anxious parents and provide psychosocial resources.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".