Using participatory research to explore the oral health awareness of junior and senior high students at L.Y. Cairns School
Bibliographic record
Abstract
Introduction. Vulnerable populations, such as school-aged adolescents with mild cognitive disabilities, can be partners in the creation of interactive learning tools. Objectives. This participatory qualitative study involved teachers and school-aged adolescents in the creation of artwork that answered 4 questions: 1) What does a healthy mouth look like? 2) What does an unhealthy mouth look like? 3) What foods keep your teeth healthy?, and 4) What are some healthy teeth habits? Methods/Results. Three teachers and their 33 students provided artwork that depicted their answers to the 4 questions, and written descriptions. The researchers coded the artwork individually and grouped the data into 4 major categories: Healthy vs. unhealthy (yellow teeth, pain); Lifestyle (no smoking, visiting the dentist), healthy habits (brushing, flossing); and Foods and Nutrition (fruits and vegetables - to eat, soda pops and pizza - to avoid). A four-minute-long video featuring animations of the students’ artwork was created professionally to showcase their knowledge and facilitate an interactive learning tool. Conclusion. Co-learning between researchers and participants fostered positive, collaborative learning within the community. Significance. This study provided insights into an effective strategy for creating oral health education tools for the learners, by the learners. The video presentation will be used by dental hygiene students to engage vulnerable populations in a discussion about oral health.
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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 | 0.031 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".