Teaching university students to cook, to improve their diet: a pilot study at Nottingham Trent University
Bibliographic record
Abstract
Objective: To determine if it is feasible to teach students to cook cheap nutritious food that they would want to reproduce in their own student residences. Design and methodology: A cohort of interested students was trained using the established ‘Let's Get Cooking’ model (www.letsgetcooking.org.uk 2012) which has been used in schools across England to establish cooking clubs within communities. These students developed a programme of four weeks of two-hour cookery lessons aimed at the student lifestyle. A pilot was run in the summer term of 2011 with lessons given to willing participants for free. The participants, both those who carried out the teaching and those who were taught were all asked to evaluate the pilot. In the following autumn a further course was run using some of the same ‘teachers’ but this time the students paid £12 (GBP) for the course of lessons. Results: The feedback from those taught to cook in the sessions was very favourable with 91% of respondents rating the sessions as excellent or good on six aspects of the course. All the participants felt their skills had improved and they felt more confident about cooking. On the second part of the pilot where the students had paid, 89% rated the sessions as excellent or good on the same criteria and 100% had enjoyed the sessions; 84% thought their skills had improved and 75% felt more confident about cooking after the course. Fifty per cent indicated that they would continue to cook healthy food after the sessions had ended. Conclusions and implications: Evidence suggests that many students have not learned to cook and do not eat healthily; however, they are prepared to pay and attend cookery classes aimed at their needs. What is not known is, if by empowering students to cook cheap healthy food, whether or not they will continue to do this after the classes have ended?
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".