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Record W3044167501 · doi:10.5539/gjhs.v12n9p145

The Association Between the Knowledge of Oral Hygiene of Mothers Who Chewing Betel Leaves and Their Oral Condition in Lingkungan II Lau Cih, Medan Tuntungan

2020· article· en· W3044167501 on OpenAlexvenueno aff
Nelly Katharina Manurung, Ngena Ria, Susy Adrianelly Simaremare

Bibliographic record

VenueGlobal Journal of Health Science · 2020
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsBetelMedicineHabitOral hygieneDentistryHygieneDietary habitTasteTraditional medicineEnvironmental healthFood sciencePsychologyBiology

Abstract

fetched live from OpenAlex

The habit of chewing betel leaves are the habit of chewing betel leaves with other additives ingredients for adding the pleasure of taste. Chewing betel leaves becomes daily habit for people, especially mothers who believe it can strengthen their teeth. This research employed an analytical survey with cross sectional design, aiming to find out the relationship between the knowledge of oral hygiene of mothers who chewing betel leaves and their oral condition. This research found that there is no relationship between the knowledge of mothers who chewing betel leaves on oral hygiene and their oral condition. However, there is association between the duration of chewing betel leaves and oral hygiene (p = 0.002) as well as caries experience (p = 0,011). Mothers who chewing betel leaves have poor oral hygiene and high caries experience.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.025
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.348
Teacher spread0.317 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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