Using a 3D food printer as a teaching tool: Focus groups with dietitians, teachers, and nutrition students
Bibliographic record
Abstract
Abstract Three‐dimensional (3D) food printing is a new technology that can be used to produce personalized and customized food products. However, very little research has been completed on how 3D food printers could be used as educational tools. As such, the objective of this study was to evaluate how teachers ( n = 6), dietitians ( n = 6), and nutrition students ( n = 11) envision the use of 3D food printers when disseminating information about food and nutrition. Focus groups were conducted with teachers, dietitians, and nutrition students. Initially, the participants were introduced to the concept of 3D food printing and then they were asked how they could use a 3D food printer in their teachings. The participants did not feel that a 3D food printer would enhance their teaching and instead felt it could confuse or frighten people. Also, all of the participants were worried about learning how to 3D print foods. The participants did state that people would be interested in watching a 3D food printer. Furthermore, the teachers and nutrition students indicated they thought a demonstration of a 3D food printer would lead to more interest in food and nutrition. Additionally, they thought a 3D food printer could be used to create visually appealing foods. Overall, until 3D food printers are found in residential and commercial kitchens, the participants did not think it would enhance their teachings; however, they did indicate that 3D food printing demonstrations could lead to students being interested in the food and nutrition fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".