Bibliographic record
Abstract
Many young adults mistakenly perceive that they have good food safety knowledge and are unlikely to experience foodborne illness. Young women’s food skills are of partic- ular importance because women are responsible for most food-related tasks in the home and many children learn food skills from their mothers. This descriptive qualitative study explored young women’s perceptions of food skills in three domains: food selection and planning, food prepara- tion, and food safety and storage. Through individual inter- views, 30 young women aged 17 to 30 years answered the following three key research questions: (i) What do food skills mean to you? (ii) How did you learn them? and (iii) In what areas are you most and least confident? Few participants mentioned food safety in their top-of- mind definition of food skills. More than half were least confident in the domain of food safety and storage. Fear prompted avoidance of cooking meat – even by those who were not vegan or vegetarian. Food skill interventions or curricula should emphasize food safety and storage so that young adults can reap the dietary and financial benefits of preparing all types of food. Consistent with others’ recom- mendations, the two most important food safety topics for educating young adults should be (i) cross-contamination and sanitation procedures and (ii) safe times and tempera- tures for cooking or storing food.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".