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Comparison between two common methods for reporting cold and diarrhoea symptoms of children in daycare centre research

2000· article· en· W2071614026 on OpenAlexaffabout
Hélène Carabin, Theresa W. Gyorkos, Julio C. Soto, L. Joseph, JP Collet

Bibliographic record

VenueChild Care Health and Development · 2000
Typearticle
Languageen
FieldHealth Professions
TopicPediatric health and respiratory diseases
Canadian institutionsCentre Integre de Sante et de Services Sociaux de LavalMcGill UniversityJewish General HospitalMontreal General Hospital
Fundersnot available
KeywordsMedicineIncidence (geometry)Common coldPediatricsDiarrheaFamily medicineDiarrhoeal diseaseDemographyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The increasing number of children attending day care centres (DCCs) in industrialized countries has refocused attention on the occurrence of infections and infectious diseases in these settings. OBJECTIVE: To evaluate the agreement between two methods (parent method vs. educator method) for reporting the occurrence of respiratory and diarrhoeal infections. METHODS: Fifty-two DCCs in Quebec, Canada, participated. Both educators and parents were invited to fill in calendars on which they would indicate the occurrence of colds and diarrhoea. For the parents' method, parents were telephoned biweekly to record their calendar information. For the educators' method, educators returned their calendar pages monthly (following prompting by phone, when necessary). RESULTS: Three hundred and thirty-three parents of toddlers participated in the 15-month reporting period between September 1996 and November 1997. The average agreement between the two methods was low (13.5% for colds and 9.8% for diarrhoea). Overall estimates of the incidence rates of respiratory and diarrhoeal infections based on parents' method were higher than those based on educators' method. CONCLUSIONS: Parents' data lead to larger estimations of incidence rates and are probably more valid than educators' data.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.137
GPT teacher head0.552
Teacher spread0.415 · 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.

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

Citations6
Published2000
Admission routes2
Has abstractyes

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