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Record W2001505058 · doi:10.1177/1010539513498766

Baby Walker Injury Awareness Among Grade-12 Girls in a High-Prevalence Arab Country in the Middle East

2014· article· en· W2001505058 on OpenAlexaff
Michal Grivna, Peter Barss, Amna Al-Hanaee, Ayesha Al-Dhahab, Fatima Alkaabi, Shamma Al-Muhairi

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2014
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of British ColumbiaInterior Health
Fundersnot available
KeywordsGovernment (linguistics)Psychological interventionPopulationMedicineMiddle EastEnvironmental healthFamily medicineDemographyGeographyNursingSociology

Abstract

fetched live from OpenAlex

Baby walkers (BWs) are a consumer product frequently associated with infant injuries. With little research in the Middle East and few population studies anywhere, female students in grade 12 in the United Arab Emirates were surveyed, assessing the prevalence of use, perceived safety, and interventions. The study population included grade-12 students in a large UAE city. Multistage random sampling selected 4/8 female Arab government schools and 3 classes each from science and arts tracks for interview by self-administered questionnaire. Response was 100%, with a total of 696 students, 55% (n = 385) of whom were Emirati citizens; 90% (n = 619) of the families used/had used BWs. Among the reasons for use, 92% reported "keeping baby safe," with 11% perceiving BWs as very safe and 74% as moderately safe. Only 16% perceived that BWs could cause injuries. Despite causing many injuries, including fatalities, BWs were perceived to be safe and used by nearly all families. Effective education of professionals, patients, the public, and decision makers is needed. Governments should consider countermeasures such as prohibiting importation, sales, and advertising, together with public education and provision of stationary activity centers.

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)0.021
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 score0.040
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.071
GPT teacher head0.333
Teacher spread0.262 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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