Over-confident and under-competent: exploring the importance of food safety education specific to high school students
Bibliographic record
Abstract
The objective of this study was to explore age-specific reasons why food safety education might be important for high school students (in Ontario, Canada), from a variety of expert perspectives. In May 2014, semi-structured key informant interviews (n = 20) were conducted with food safety and youth education experts. A thematic analysis of verbatim transcripts of the interviews was conducted. Participants identified three major reasons why food safety is important for high school students: (i) they have current and personal needs for food safety information, (ii) high school is an ideal time and place to instill life-long good habits, and (iii) they are part of the foodborne illness risk landscape. Food safety education was deemed important for high school students, who were seen as a unique and captive audience in need of safe food handling skills, now and in the future, for a variety of reasons: potential employment advantages, improved food literacy, combating their sense of “invincibility,” and helping instill essential life skills that they may not get elsewhere. These results confirm the importance of food safety education for high school students and highlight the need to determine age-appropriate interventions and methods to engage high school students and improve their safe food handling practices.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".