Comparison of pain, posture, physical activity and sleep quality of undergraduate and graduate students of health sciences
Bibliographic record
Abstract
The aim of this study was to investigate pain, posture, levels of physical activity, and sleep quality in undergraduate and postgraduate students along with determine how educational differences affect these parameters. A cross-sectional study. This study conducted with the participation of 60 undergraduate, 58 graduate students. The Short-Form McGill pain questionnaire was used to assess pain, the New York Posture assessment for posture assessment, the International Physical Activity Questionnaire (IPAQ) to determine physical activity levels, and the Pittsburgh Sleep Quality Scale to determine sleep quality. A total of 118 participants (mean age =24.07 years, 72.9% women) were included in the study. Posture and the low IPAQ score had significant differences between undergraduate and graduates (p<0.05), whereas pain, IPAQ total score, and sleep quality were similar (p>0.05). When the literature is briefly mentioned, there are general studies about university students, but it was not clear how the undergraduate and postgraduate status of the students affected the health related factors. Therefore, based on the results of this study, necessary evaluations and improvements should be made regarding the risk factors faced by the students, taking into account the pain, posture, physical activity levels, and sleep quality of undergraduate and postgraduate students.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".