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Record W2001032494 · doi:10.1002/ppul.20576

Internet and written respiratory questionnaires yield equivalent results for adolescents

2007· article· en· W2001032494 on OpenAlexaff
Hein Raat, Resiti T. Mangunkusumo, Ashna D. Hindori‐Mohangoo, E. F. Juniper, Johan van der Lei

Bibliographic record

VenuePediatric Pulmonology · 2007
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMcMaster University Medical Centre
FundersErasmus+ZonMw
KeywordsMedicineAsthmaFamily medicineThe InternetAllergyPopulationPediatricsInternal medicineImmunologyEnvironmental health

Abstract

fetched live from OpenAlex

This study compared results from Internet and written questionnaires about respiratory symptoms in order to find out if both forms of the survey yielded the same answers. One thousand seventy-one students, ages 13 to 17, were asked to complete either an Internet or a written questionnaire. The demographic characteristics of the participants equalled those of the general Dutch adolescent population. Participants were randomly assigned to fill out an electronic or written questionnaire. In addition to eight items from the International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire, two items on doctor visits (medical attention) regarding asthma or allergic disease during the past 12 months were included. The participation rate was 87%. The Internet version of the questionnaire showed fewer missing answers than the written version, but this was not statistically significant. The respiratory items did not show statistically significant score differences between the Internet and written modes of administration, and there was no visible trend for higher respectively lower scores by either mode of questionnaire administration. From these results, we conclude that respiratory questionnaires may be provided to adolescents electronically rather than on paper, since both approaches yielded equal results. To generalize these findings, we recommend repeated studies in other settings.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.298
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2007
Admission routes1
Has abstractyes

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