Application of the Theory of Planned Behavior to Predict Dental Attendance and Caries Experience among Children of Newcomers
Bibliographic record
Abstract
Background: This study aimed to explore the predictors of regular dental attendance behavior and caries experience among children of newcomers to Canada using the Theory of Planned Behavior (TPB). Methods: A total of 274 newcomer parents with a child aged 1 to 12 years old who had lived in Canada for 10 years or less participated in this cross-sectional study. Children underwent a dental examination to measure their deft/DEFT, and parents completed a self-administered questionnaire. Parental attitudes, subjective norms, perceived behavioral control (PBC), and intention were examined using Structural Equation Modeling (SEM) as predictors of dental attendance behavior and caries experience using the TPB model. Results: Out of all the components of the model, attitude and PBC significantly predicted the intention (p < 0.05) while the subjective norm had no statistical effects on the loading of the model (p > 0.05). Intention significantly predicted behavior, but behavior could not predict caries experience. Although 51.6% of the variance of intention was predicted by this model, only 0.2% of the variance for dental attendance behavior was explained. Conclusions: The theory of planned behavior was successful in predicting dental attendance intention and behavior for children among newcomers, but this behavior could not predict caries experience in children.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".