Life habits of overweight and obesity in the pandemic period by COVID-19
Bibliographic record
Abstract
Background: the pandemic caused by COVID-19 forced the population to be confined and socially distanced for a long period of time, for which reason they opted to modify their lifestyle habits. The personnel most at risk of suffering changes in their daily lifestyles and habits were healthcare personnel. Objective: to identify the lifestyle habits predisposing to develop overweight and obesity during the COVID-19 pandemic in health personnel of the Pediatrics service of the Hospital General Ambato. Methods: observational, descriptive, cross-sectional study, using a survey comprising sociodemographic data, anthropometric measurements recorded in 2019 and 2022 from the occupational medical records of health personnel, and the FANTASTIC questionnaire, designed by the Department of Family Medicine at McMaster University in Canada, adapted and validated by specialists in Spanish, to measure and identify the lifestyles of people. Results: 38,09 % have a normal body mass index in relation to a regular lifestyle, while one person had grade I obesity and presented a bad FANTASTIC, however, there is 28,57 % of the population with a regular lifestyle habit and overweight. Conclusions: the results of this study indicate that, during confinement, the dietary and lifestyle habits in health personnel of the Pediatrics service of the Ambato General Hospital underwent transitions, with a tendency towards regular eating habits and a tendency towards a more regular lifestyle
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".