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Record W4320727699 · doi:10.3390/biomed3010011

Association between Family Level Influences and Caries Prevention Views and Practices of School Children in a Sub-Urban Nigerian Community

2023· article· en· W4320727699 on OpenAlexaff
Abiola Adeniyi, Morẹ́nikẹ́ Oluwátóyìn Foláyan, Olaniyi Arowolo, Nneka Maureen Chukwumah, Maha El Tantawi

Bibliographic record

VenueBioMed · 2023
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLogistic regressionMedicineOddsOdds ratioDemographyCross-sectional studyPopulationEnvironmental healthFamily medicine

Abstract

fetched live from OpenAlex

Little is known about how family-level factors influence children’s caries prevention views and practices in Nigeria. The purpose of this study was to assess the associations between family level characteristics and caries prevention views and practices of 6–11-year-old primary school children. Data was collected through a cross-sectional survey of 1326 children in Ile-Ife, a Nigerian suburb. The child’s family structure, size, and birth rank were independent variables while the child’s caries prevention views and self-care practices were dependent variables. Multivariable logistic regression analysis was conducted to identify risk indicator(s) for caries prevention views and practices. The study participants’ mean (SD) age was 8.7 (1.9) years, 407 (30.7%) children had positive caries prevention views, and 106 (8.0%) children did not use the recommended self-care caries preventive methods. Children from larger families had significantly lower odds of having positive prevention views (AOR: 0.572; p = 0.002). Children who were not living with both parents had higher odds of using recommended self-care caries preventive methods (AOR: 3.165; p = 0.048). The findings suggest that family size and family living structure may be social determinants of caries risks in children 6–11 years old in the study population. These findings need to be studied further.

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)0.001
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 score0.007
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.109
GPT teacher head0.390
Teacher spread0.281 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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