Limited agreement between written and video asthma symptom questionnaires
Bibliographic record
Abstract
The prevalence of asthma remains difficult to determine with precision with no absolute or "gold" standard for diagnosis. A recently developed video questionnaire for epidemiological studies with less reliance on understanding written questions provides another tool for determining prevalence and severity of asthma. This report from the International Study of Asthma and Allergies in Childhood (ISAAC) examines the agreement between the ISAAC video questionnaires on respiratory symptoms and reported asthma. Between December 1993 and April 1995, 4952 children aged 13-14 years in two Canadian communities completed sequentially the ISAAC written and video questionnaires at school. The agreement between responses to the two questionnaires for reported wheeze ever, current wheeze, wheeze on exercise, and nocturnal wheeze (the latter three questions relating to symptoms in the last 12 months), and to any combination of the latter three questions was examined in the full sample and in those reporting diagnosed asthma, using concordance and kappa coefficients as measures of agreement. The prevalences of wheeze ever, current wheeze, wheeze on exercise, and nocturnal wheeze were significantly lower based on responses to the video questionnaire compared with the written questionnaire in both regions in the full sample and in those labeled as having asthma. Although concordance between video and written questionnaires always exceeded 60% and often exceeded 70% for related questions, agreement measured by the kappa statistic for each question was only fair to moderate (kappa = 0.22-0.51). We conclude that the video questionnaire yields lower reported prevalence rates for asthma symptoms, and that there is limited agreement between responses to the two questionnaires that is not explained by issues of language, culture, or literacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".