Perceived and Assessed Dental Treatment Needs of Schoolchildren in Benoe Division, Cameroon
Bibliographic record
Abstract
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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".