The prevalence of neck pain, back pain and low back pain among third-year medical students at universities in the metropolitan region of Porto Alegre in times of Covid-19
Bibliographic record
Abstract
Objective: To determine the prevalence of cervical, dorsal and lumbar pain caused by switching to remote classes during the Covid-19 pandemic. Methods: This is an original article based on a cross-sectional study among men and women over the age of 18 who are third-year medical students to assess the incidence of neck pain, back pain and low back pain, using online forms with questions about physical and mental health. Results: Around 60% of the participants said they had adapted their study environment because of the remote classes, with a further 70% saying they were attending classes in the office, with their backs poorly supported. In addition, there was a low number of overweight students who performed daily stretching. Conclusion: The data was analyzed using tables, descriptive statistics and the statistical test: Mann-Whitney Non-parametric Test and Krsukal-Wallis Non-parametric Test and the importance of further research was highlighted, given that this research topic is essential for the prevention of possible comorbidities.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".