Low back pain among doctors in a tertiary institution in Southern Nigeria
Bibliographic record
Abstract
Introduction: Low back pain is common in health workers with deleterious effects on their work and quality of life. Aims: This study aimed to identify work related disabilities and risk factors for low back pain amongst doctors in Nigeria. Methodology: One hundred and fifty-four doctors were recruited and a structured proforma was administered using the Aberdeen low back pain scale, revised Oswestry and Quebec pain scales as guides. Data was analysed using Statistical Package for the Social sciences version 25. Univariate and multivariable logistic regression were used to calculate the odds ratios for the independent risk factors for LBP. Level of significance was determined at p < 0.05. Results: The male to female ratio was 1.8:1 and 70(45.50%) doctors were in the age range of 31-40years. Half (50%) of the respondents were obese while 21.9% were overweight. The duration of an episode of back pain was less than a week in 130 (84.40%) persons. A few doctors- 22(14.29%) reported that low back pain had prevented them from coming into work, of these, 12 had been absent for a day, four for 2-7 days and six for 1 to 4 weeks. Anesthetists were ten times more likely to develop low back pain than any other medical specialty (OR=10.99, 95%CI=1.336-90.545, p=0.026) and increasing age and BMI were also identified as predictors of low back pain. Conclusion: Low back pain is associated with poor productivity among doctors and can impact health care delivery.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".