Measurement properties of the Health Anxiety by Proxy Scale (HAPYS): A new questionnaire to assess parents' worries about their child's health
Bibliographic record
Abstract
OBJECTIVE: Health anxiety by proxy refers to parents' excessive worries about their child's health. The Health Anxiety by Proxy scale (HAPYS) is a new self-report questionnaire to assess parents' worries and behaviors regarding their child's health. This study aimed to investigate the measurement properties of the HAPYS. METHODS: Questionnaires were completed by 204 parents, and a HAPYS score was obtained for 200 parents: 39 parents diagnosed with health anxiety, 33 parents with different anxiety disorders, 33 parents with a Functional Somatic Disorder, and 95 healthy parents. We evaluated the following measurement properties: structural validity, reliability, convergent validity ((pain catastrophizing, parents' reports of child's emotional and physical symptoms), discriminant validity (parental reports of child's well-being), and known-groups validity (see compared groups above). RESULTS: HAPYS demonstrated a one factor dimensionality, and excellent internal reliability (α = 0.95; CI: 0.93-0.97) and test-retest reliability after two weeks (ICC = 0.91; CI: 0.87-0.94). Convergent validity with the construct of parental catastrophizing about child pain was good (r = 0.72; CI: 0.64-0.78)). Good known-groups validity was demonstrated by the largest total HAPYS score observed in parents with health anxiety (median = 35; IQR: 9-53) and the lowest score in healthy parents (median = 9; IQR: 5-15) (p < 0.001). CONCLUSION: The findings support that HAPYS is a useful measure of health anxiety by proxy. Future research should examine the measurement properties in larger samples and different languages with further statistical analyses of structural validity.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".