Identifying Children with Dental Care Needs: Evaluation of a Targeted School‐based Dental Screening Program
Notice bibliographique
Résumé
OBJECTIVES: It has been suggested that changes in the distribution of dental caries mean that targeting high-risk groups can maximize the cost effectiveness of dental health programs. This study aimed to assess the effectiveness of a targeted school-based dental screening program in terms of the proportion of children with dental care needs it identified. METHODS: The target population was all children in junior and senior kindergarten and grades 2, 4, 6, and 8 who attended schools in four Ontario communities. The study was conducted in a random sample of 38 schools stratified according to caries risk. Universal screening was implemented in these schools. The parents of all children identified as having dental care needs were sent a short questionnaire to document the sociodemographic and family characteristics of these children. Children with needs were divided into two groups: those who would and who would not have been identified had the targeted program been implemented. The characteristics of the two groups were compared. RESULTS: Overall, 21.0 percent of the target population were identified as needing dental care, with 7.4 percent needing urgent care. The targeted program would have identified 43.5 percent of those with dental care needs and 58.0 percent of those with urgent needs. There were substantial differences across the four communities in the proportions identified by the targeted program. Identification rates were lowest when the difference in prevalence of need between the high- and low-risk groups was small and where the low-risk group was large in relation to the high-risk group. The targeted program was more effective at identifying children from disadvantaged backgrounds. Of those with needs who lived in households receiving government income support, 59.0 percent of those with needs and 80.1 percent of those with urgent needs would be identified. CONCLUSIONS: The targeted program was most effective at identifying children with dental care needs from disadvantaged backgrounds. However, any improvements in cost effectiveness achieved by targeting must be balanced against inequities in access to public health care resources.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».