Brighter Smiles Africa - Translation of a Canadian Community-based Health-promoting School Program to Uganda
Bibliographic record
Abstract
PROJECT GOAL: To adapt a successful Canadian health-promoting school initiative to a Ugandan context through international partnership. RATIONALE: Rural children face many health challenges worldwide; health professionals in training understand these better through community-based learning. Aboriginal leaders in a Canadian First-Nations community identified poor oral health as a child health issue with major long-term societal impact and intervened successfully with university partners through a school-based program called "Brighter Smiles". Makerere University, Kampala, Uganda (MUK) sought to implement this delivery model for both the benefit of communities and the dental students. KEY STEPS/HURDLES ADDRESSED: MUK identified rural communities where hospitals could provide dental students with community-based learning and recruited four local schools. A joint Ugandan and Canadian team of both trainees and faculty planned the program, obtained ethics consent and baseline data, initiated the Brighter Smiles intervention model (daily at-school tooth-brushing; in-class education), and recruited a cohort to receive additional bi-annual topical fluoride. Hurdles included: challenging international communication and planning due to inconsistent internet connections; discrepancies between Canadian and developing world concepts of research ethics and informed consent; complex dynamics for community engagement and steep learning curve for accurate data collection; an itinerant population at one school; and difficulties coordinating Canadian and Ugandan university schedules. ACCOMPLISHMENTS: Four health-promoting schools were established; teachers, children, and families were engaged in the initiative; community-based learning was adopted for the university students; quarterly team education/evaluation/service delivery visits to schools were initiated; oral health improved, and new knowledge and practices were evident; an effective international partnership was formed providing global health education, research and health care delivery.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".