Denial or decay? The effects of an oral health educational program on caregivers of preschool children
Bibliographic record
Abstract
Background: Dental caries (tooth decay or cavities) are perhaps the most prevalent infectious disease impacting our nation’s children. Dental caries in children are five times more common than asthma and seven times more common than hay fever. More than 40% of children have tooth decay by the time they reach kindergarten. The aim of this study was to examine the effects of an oral health educational program on knowledge and behavior-specific cognitions and affect in caregivers of children ages two to five. Hypotheses: The five hypotheses determined if there was a positive relationship between: (1) prior related behaviors and behavior-specific cognitions and affect, (2) personal factors and behavior-specific cognitions and affect, (3) pre- knowledge and prior related behaviors, (4) post-knowledge and intent to provide oral health care, and (5) the difference between caregivers’ pre-intervention and post-intervention scores on knowledge. Methods: Descriptive quasi-experimental study utilizing a pre-test post-test model, with a convenience sample of 400 caregivers recruited from seven Head Start program sites in South Florida, who attended one of 18 oral health educational programs. Research questions addressed the relationship between the oral health educational program and the dependent variables prior related behavior, personal factors, behavior-specific cognitions and affect, knowledge, and intent. Care- givers completed a demographic survey and an oral health behavior questionnaire, a knowledge pre-test, then viewed a 16-minute video designed by Colgate, and completed another knowledge post-test. Results: The educational program had an overall significant effect on caregivers’ prior related behaviors, r = .43, p (two-tailed) < .01; behavior-specific cognitions and affect, r = .43, p (two-tailed) < .01; intention to provide oral health care for their children, r = .27, p (two-tailed) < .01; and post-test for knowledge of oral health care ( M = 60.57, SE = .30) compared to pre-test for knowledge ( M = 59.03, SE = .26), t (399) = -6.35, p < .01, r = .30). However, only 0.4% ( R 2 = .004, Adj R 2 = .00) of the variance in the outcome variable was accounted for by the predictor variables; the model was not significant, F (2, 397) = .80, p = .49 in comparing personal factors and behavior-specific cognitions and affect. Conclusions: Poor oral health can compromise a child’s overall health and impact their growth and development. Therefore, effective educational programs should be initiated early on to combat widespread oral disease. Effective educational interventions will contribute to positive behavior change and increased knowledge in caregivers. Through health/public policy change, future research will promote preventive oral health care and decrease oral disease .
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".