Hubungan Indeks Massa Tubuh (IMT) dengan Aktivitas Fisik pada Mahasiswa Kedokteran
Bibliographic record
Abstract
Background and Objectives: Body Mass Index (BMI) is a parameter established by WHO (World Health Organization) as a ratio of body weight to body height squared. Physical activity according to WHO is any body movement produced by skeletal muscles that require energy. Lack of physical activity) is an independent risk factor for chronic disease, and is thought to cause death globally. The aim of this study was to determine the relationship between BMI and physical activity. Methods: This study is a descriptive research design. The data was collected by using a questionnaire. The data of this study were categorical variables from 2 groups so it used the Chi-Square test. Reference results were entered into the Mendeley application using the Vancouver system. Results: total sample was 104 people with a mean age of 21-23 years. 21 years 22 people (21.2%), 22 years 46 people (44.2%), 23 years 23 people (22.1%). Based on gender, it was found that there were 37 men (35.6%) and 67 women (64.4%). Based on BMI, it was found that 13 people were underweight (12.5%), 45 people were normal (43.3%), 24 people were overweight (23.1%), 20 people were obese 1 (19.2%), and 2 people were obese 2 ( 1.9%). Conclusion: There is no relationship between BMI and physical activity.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".