Perceived and Assessed Dental Treatment Needs of Schoolchildren in Benoe Division, Cameroon
Notice bibliographique
Résumé
INTRODUCTION: Oral health surveys combining clinical and subjective measures are effective to inform oral health policy, practice, and evaluation of oral health interventions. However, only a few studies have examined the agreement between these measures in developing countries. OBJECTIVES: This study investigates dental treatment needs among Cameroon's schoolchildren; specifically, we aim to estimate the extent to which perceived and clinical measures are in agreement. METHODS: Using a multistage sampling technique, we randomly selected 11 schools and their pupils to participate in this study. We conducted an oral clinical examination using a mouth mirror and blunt probe in a classroom to evaluate children's oral health. In addition, the participants filled out a questionnaire on sociodemographic characteristics, oral health behavior, and perceived treatment needs. To fulfill our aims, we use descriptive statistics and unconditional logistic regression. RESULTS: Out of 700 children invited to participate, 692 completed the study (98.8%). The mean age of the children was 11.45 y (SD = 1.21), and there were slightly more boys ( n = 366, 52.9%) than girls ( n = 326, 47.1%). The majority of the children (85.2%) felt that their oral health was good, and more than half (53.2%) reported a perceived need for dental treatment. While 68.2% ( n = 472) had at least 1 objective treatment need, only 65.8% of them perceived this need, indicating a medium level of sensitivity (65.9%, 95% CI = 61.4% to 70.2%). In addition, we observed a high positive predictive value (84.5%, 95% CI = 80.4% to 88.1%) for perceived treatment need to detect clinically evaluated dental treatment need. CONCLUSION: Our findings show that perceived treatment has a high positive predicted value to determine clinical treatment need. Subjective assessment of treatment need may be an alternative low-cost option to help policy makers to design oral health interventions for Cameroonian children. KNOWLEDGE TRANSFER STATEMENT: This study illustrates the potential of schoolchildren in a low-income country to make a good prediction of their dental treatment needs. The majority of these countries lack the human and material resources to conduct oral health surveys that include clinical assessment of treatment needs. Therefore, stakeholders can rely on data from self-administered oral health surveys to inform policy and delivery of services to schoolchildren in resource-limited settings.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».