Bibliographic record
Abstract
The current global coronavirus pandemic (COVID-19) has forced many countries to enforce national lockdowns and periods of self-isolation in an attempt to stem the virus from being transmitted between humans, aiming for fewer deaths from the highly-infectious disease. The United Kingdom has undergone three of these national lockdown periods, during which individuals will have found themselves spending extended periods of time in their own homes, ultimately sacrificing many freedoms and potentially even their health. Participants (n=365) of this study were recruited, using social media advertisements, to self-report changes in their eating habits from the beginning of the pandemic as a result of the lockdowns and periods of self-isolation. An anonymous online questionnaire, targeting the UK adult population was completed. The results identified several positive changes made by individuals but the pandemic had mostly a detrimental impact on the population’s eating habits. Over a quarter of participants reported that the pandemic had a positive effect on their diet overall; 23% reported an increase in homemade meals, 22% reported fewer takeaways consumed and 27% said they gave more nutritional consideration to their diet. Thirty-two percent reported an improvement in their own cooking ability and 16% felt they succumbed to emotional eating less often. Negative health trends appeared to be more abundant in this survey. Findings highlighted that more than a third of participants were consuming more takeaways while at home as well as more than half agreeing that it was easier to have a takeaway delivered. The study also found that 22% of participants were cooking fewer homemade meals than they did beforehand and almost a third struggled to get hold of the groceries they needed during this time. The study also found that 44% of people changed their spending habits when it came to buying groceries although there was no significant correlation between personal finances and eating habits. These findings suggest that eating habits have been affected greatly by the COVID-19 pandemic and the government-enforced lockdowns, and more often in a negative way. It may be that individuals were not aware of the effect the pandemic is having on their nutritional health and therefore more should be done from major bodies to ensure that awareness is raised and that the lockdowns have proven to be beneficial to some individual’s health and the reasons for this should be broadcast nationwide.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".