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Record W4224248421 · doi:10.53730/ijhs.v6ns3.6311

A study on the evaluation of tooth brushing skills and its relation with the age and gender of children

2022· article· en· W4224248421 on OpenAlexaff
Purobi Choudhury, Rohit Kumar Singh, Vinod Patel, Ashok Kumar, Kundan K. Singh, Vijayendra Pandey

Bibliographic record

VenueInternational Journal of Health Sciences · 2022
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsTooth brushingMedicineHabitDentistryDevelopmental psychologyPsychologyRelation (database)Social psychologyToothbrush

Abstract

fetched live from OpenAlex

The present study was undertaken for studying tooth brushing skills and its relation with the age and gender of children. A total of 100 school going children were assessed during the study period. A questionnaire was framed and was given to all the participants. The questionnaire included detailed knowledge about their brushing habits. Type of brushing habit was assessed among all the children. Also assessment was done in relation to age and gender. All the results were analysed by SPSS software. 41 percent of the subjects gave history of brushing once daily. Combined method of brushing was seen in 36 percent of the subjects while 35 percent of the subjects gave history of brushing with horizontal method. Non-significant results were obtained while assessing the tooth brushing skills in relation with the age and gender of children. Brushing regularly is a low-cost, effective strategy to reduce the risk of childhood caries. Community-based efforts can help parents achieve this important health behaviour.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.083
GPT teacher head0.419
Teacher spread0.336 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2022
Admission routes1
Has abstractyes

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