Back Pain Among Health Care Workers in a Saudi Aramco Facility: Prevalence and Associated Factors
Bibliographic record
Abstract
The objective of this study was to identify risk factors for back pain among health care workers of Saudi Aramco. A validated questionnaire was used to collect information on back pain in the last 12 months as well as relevant risk factors among health care workers at a single Saudi Aramco health care facility. Completed responses were received from 964 of 3,295 workers. Three significant predictors for the presence of back pain were identified: female gender (odds ratio [OR] = 1.9, 95% confidence interval [CI] = 1.3-2.7), Saudi nationality (OR = 2.3, 95% CI = 1.4-3.9), and working as a surgeon (OR = 5.4, 95% CI = 1.4-21.5). Educational level was of borderline significance (OR = 1.6, 95% CI = 0.98-2.7). An interaction between gender and race was identified, with Saudi females being at particularly high risk of reporting back pain (OR = 3.9, 95% CI = 1.8-8.5). Gender, occupation, and nationality were identified as risk factors for back pain, and a particularly high risk was seen among female Saudis health care workers in Saudi Aramco. Nationality may be important because of cultural difference between groups, but also because of differences in benefits available.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".