The impact of the COVID-19 pandemic on overweight and obesity: the case of NEET in Türkiye
Bibliographic record
Abstract
The study aims to investigate how COVID-19 pandemic affects the probability of being overweight or obese in the case of NEET in Türkiye. This study delves into the comprehensive dataset provided by the Turkish Health Research Surveys from 2014 to 2022. The primary focus is on individuals aged 15–29, with a specific exploration of the repercussions of the COVID-19 pandemic on health conditions. The investigation centers on deciphering the intricate connections between NEET (Not in Education, Employment, or Training) status, pandemic ramifications, and the likelihood of being overweight or obese. Leveraging probit models and meticulous control for variables such as age, gender, regional fixed effects, and completed education levels, our findings indicate significant associations. Notably, the NEET subgroup demonstrates a 5.8% increase in the probability of being obese or overweight. We find suggestive evidence that the pandemic contributes to a marginal increase of 1.2%. A particularly intriguing revelation surfaces as being NEET significantly amplifies the likelihood of overweight or obesity in young females. Education plays a vital role in preventing overweight or obesity, as higher education levels correlate with a lower likelihood of obesity. Moreover, we analyze different age groups, our results show that being NEET is positively associated with the probability of overweight or obesity for females aged 19–24 and 25–29 in the post-pandemic period, indicating higher sensitivity to the pandemic among NEET females.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".