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Compliance with Fluoride Supplements Provided by a Dental Hygienist in Homes of Low‐Income Parents of Preschool Children in Quebec

2007· article· en· W2061148857 on OpenAlexaffabout
Fabien Gagnon, Pierre Catellier, Isabelle Arteau‐Gauthier, Élisabeth Simard‐Tremblay, Marianne Lepage‐Saucier, Nina Paradis‐Robert, J. Michel, André Lavallière

Bibliographic record

VenueJournal of Public Health Dentistry · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFluoride Effects and Removal
Canadian institutionsCégep de Baie-ComeauUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineFluorideCompliance (psychology)Intervention (counseling)Family medicinePregnancyLow incomeEnvironmental healthDentistryNursingPsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aimed to assess the compliance with fluoride supplements provided at home by a dental hygienist to mothers of at-risk preschool children. METHODS: Participants were recruited during pregnancy of low-income women. On the first visit, the mothers of 60 infants aged 6 to 9 months were handed free fluoride supplements. A questionnaire was administered at that time and after 6 and 12 months to assess compliance during the preceding week. RESULTS: At the beginning of the study, none of the mothers reported having given fluoride supplements, in comparison with 73 percent of mothers of 44 infants who received all three visits at the end of follow-up; 48 percent reported fluoride supplement use on a daily basis. CONCLUSIONS: Removal of financial and physical barriers and personal professional involvement are good strategies to achieve compliance with fluoride supplements. Further assessment regarding the possible application of this intervention to other professional or cultural contexts is warranted.

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)0.002
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 score0.210
Threshold uncertainty score0.423

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0010.000

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 head0.013
GPT teacher head0.287
Teacher spread0.274 · 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

Citations5
Published2007
Admission routes2
Has abstractyes

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