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Enregistrement W6903101745 · doi:10.7939/81987

Sociocultural Determinants of Children’s Oral Health Among Immigrants: Developing and Testing a Conceptual Model

2025· dissertation· en· W6903101745 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity of Alberta Library · 2025
Typedissertation
Langueen
DomaineDentistry
ThématiqueDental Health and Care Utilization
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAcculturationSociocultural evolutionImmigrationConceptual modelEthnic groupStructural equation modelingVulnerability (computing)Oral healthConstruct (python library)

Résumé

récupéré en direct d'OpenAlex

Abstract Background: The Canadian Collaboration for Immigration and Refugee Health highlights oral health diseases among the top 11 health challenges for immigrants and refugees. Foreign-born individuals face higher vulnerability due to migration-related disruptions and limited dental access. Cultural shifts, known as "acculturation," impact immigrants' health, varying in degree. Understanding this requires considering post-migration socio-cultural context. Social connections change post-migration, affecting oral health, well-being, and quality of life. Recognizing these shifts is crucial for stakeholders: dentists, community workers, and researchers. Social support is vital for new immigrants, aiding adaptation, and healthcare access. Both parental acculturation and support shape children's oral health. Their combined impact on oral health remains underexplored in existing literature. Objective: The overarching objective of this research was to construct and assess a conceptual model aimed at predicting oral health behaviors and caries experience of immigrants’ children. The goal was to develop a model that explains the sociocultural factors influencing children’s oral health among immigrants, using Structural Equation Modeling (SEM). Methods: This study unfolded in three phases, beginning with ethics approval from the University of Alberta Research Ethics Board (Protocol # Pro00072345). The first phase encompassed two systematic reviews: one focused on acculturation's impact on oral health among immigrants and ethnic minorities, while the other explored social support's influence on oral health in these groups. The second phase, a cross-sectional study, investigated how parental acculturation and perceived social support affected their children's oral health behaviors and caries experience. Participants included first-generation immigrant parents residing in Canada for two or more years, with children aged 2–12 years. Data collection took place in convenient community settings through multilingual community workers using non-probability snowball sampling. Parents provided demographic, perceived social support, acculturation, and children's oral health behavior data. Trained dentists conducted dental exams and used the DMFT/dmft index to assess caries experience. Oral health behaviors were measured with an eight-item questionnaire. The main independent variables were parents' perceived social support (PSS), measured using the validated Personal Resource Questionnaire (PRQ2000) and parents' acculturation and strategies were evaluated with the Asian American Multidimensional Acculturation Scale (AAMAS). The data collected in the second phase informed the creation of a conceptual model in the third phase, aimed at predicting immigrant children’s oral health behaviors and caries experience through Structural Equation Modeling (SEM), examining parental acculturation and perceived social support's influences. Results: A total of 336 parent/child pairs participated in the study. The average parental acculturation level was 10.46, and the average perceived social support (PSS) score was 63.27. Factors like length of residency, parents' education, and household income significantly predicted acculturation level. Parents with higher Canadian cultural knowledge reported more frequent children's toothbrushing. Parents of children consuming >1 sugary item/day had higher acculturation levels, English language proficiency, and Canadian food adoption. Parents of recent dental visitors reported higher assimilation and lower separation scores, while those visiting due to problems had higher marginalization scores. Parental acculturation wasn't significantly linked to children's dental decay (DMFT/dmft). Household income predicted parental PSS (B = -5.69). Children of parents with higher PSS brushed teeth ≥2/day. Parental education predicted social integration and nurturance; income predicted social integration, worth, and assistance. Parents with more intimacy and social integration were more aware of children's oral health. Parental social integration scores were higher when children consumed ≥1 sugary snack/day. All domain scores were higher when children brushed teeth ≥2/day. Structural Equation Modeling (SEM) indicated 77% of DMFT/dmft variance was explained by parental PSS, acculturation, predisposing/enabling factors, and children's oral health (OH) behaviors. Parental PSS had a direct effect on reduced dental caries and sugar consumption. Parental acculturation mediated by positive OH behaviors increased caries risk. Conclusions: The SEM analysis found significant variance in immigrants’ children's caries experience. Findings highlight parental acculturation and PSS levels predicting oral health behaviors and caries. Recognizing sociocultural factors is vital for stakeholders—dentists, community workers, and researchers. Immigrants' vulnerability to oral health issues underscores the need for deeper exploration and expanding the model.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,011
score de la tête « metaresearch » (Gemma)0,016
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,031
Score d'incertitude au seuil0,061

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0110,016
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,004
Bibliométrie0,0050,004
Études des sciences et des technologies0,0020,003
Communication savante0,0050,004
Science ouverte0,0030,005
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,020
Tête enseignante GPT0,260
Écart entre enseignants0,239 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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