Hubungan Beberapa Faktor dengan Keluhan Nyeri Punggung Bawah pada Karyawan Kantor
Bibliographic record
Abstract
The office is the neighborhood where work and must be fulfilled the provisions in the workplace ( health, safety and welfare ). Although the office in general is considered safe, but in it still contain health danger that can lead to serious injury. One form of a nuisance that can arise due to a work environment especially in a work environment office is pain the lower back. Population in this research are employees stikes hang tuah pekanbaru who works as a staff of academic.While a sample in this research is the whole population of which satisfies the criteria as a sample in research. The aim of this research to analyze the relationship of Ergonomic Desk Chair and Posture With Low Back Pain on Office Employees. The research method is Analytic Cross Sectional Study with sampling method and 63 employes as sample. Independent variable of this research is body mass index, age, posture, ergonomic desk chair, working time and working olg. Analysis is doing by univariat, bivariate and multivariate. The result obtained is 3 variables that relationship With Low Back Pain on Office Employees that is, body mass index, age, posture. From multivariate analysis acquired that bad posture the most dominant variable that relationship with Low Back Pain, 40 time having low back pain more than worker that good posture (CI 95% : OR = 40).
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".