“<i>My Roommates Would Laugh at me</i>”: Young Males Reveal Embarrassment over Lack of Food Skills
Bibliographic record
Abstract
Purpose: This descriptive qualitative study explored young males’ perceptions of food skills in 3 domains: food selection and planning, food preparation, and food safety and storage. Methods: Semi-structured interviews were audio-recorded and transcribed verbatim. Data were analyzed using the constant comparative method. Results: Forty-four young men (aged 17–35) reported varying levels of food skills, from little/no confidence to very confident and skilled. Most participants learned food skills from their mothers. Greater involvement in food selection and planning at a young age appeared to be related to parental influence and encouragement, exposure to food skills at school, and interest in food-related activities, which, in turn, provided a solid foundation for being confident cooks as young adults. Most notable was the lack of knowledge about, or confidence in, food safety and storage. Young men with low self-perceived food skills were deeply embarrassed about this deficiency in front of peers who had higher levels of confidence and skills. Conclusions: Future interventions or curricula should emphasize food safety and storage. This research also illustrates the importance of the home environment in teaching food skills to youth and ensuring that food skills are taught well before young adults begin living independently.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".