Implementing a Secondary Database as a Teaching Tool to Improve Genomic Literacy Among Dental Students
Bibliographic record
Abstract
BACKGROUND: Recent advancements in precision medicine and precision dentistry have necessitated genomic literacy in healthcare professionals. Both the knowledge of genetics and data in primary biological databases are rapidly expanding beyond what is presented in textbooks. Dental students are often unfamiliar with the growing field of biological data and the tools used to analyse and interpret genetic information. APPROACH: To improve genomic literacy among dental students, we incorporated 'Bioinformatics for Dentistry', a dental-specific secondary database, as a teaching tool in the first year of the Doctor of Dental Surgery (DDS) program. This study aims to explore students' perspectives on using a secondary database as a tool for teaching and learning. EVALUATION: A convergent, parallel mixed-method study was conducted to explore student perception of the database as a teaching tool. Qualitative and quantitative data were collected from students' reflection assignments and surveys. Descriptive statistics and manifest content analysis were applied to analyse the survey data and reflection assignments, respectively. All (100%) students (n = 32) completed the assignment with reflective answers; 38% (n = 12) of the class completed the voluntary survey. Survey participants indicated that 'Bioinformatics for Dentistry' was easy to navigate and helpful for learning the genetics of tooth development. Codes from qualitative data were grouped into three categories, representing the benefit of the secondary database attributed by the students. IMPLICATIONS: Dental students positively valued the use of 'Bioinformatics for Dentistry' to learn the genetics of tooth development. This secondary database can improve genomic literacy to meet the challenge of the postgenomic era.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".