The efficacy of pilates based therapeutic exercise along with ergonomic interventions in non-specific chronic low back pain among nursing professionals
Bibliographic record
Abstract
Background: Low back pain poses serious challenge to nursing profession due to their prolonged standing, improper posture and heavy lifting activities. Thus treating low back pain in nurses is important in order to improve their working efficiency and physical wellbeing. Aim: To investigate the efficacy of Pilates based therapeutic exercise along with ergonomic interventions with non-specific chronic low back pain in nursing professionals OBJECTIVE: To evaluate the efficacy of the Pilates based therapeutic exercise along with ergonomic interventions in relieving pain and disability. Methodology: twenty physically active nurses between 20 and 45 years old with chronic LBP were taken into Experimental group. The specific exercise training group participated in a 12-week program consisting of training on Pilates based therapeutic exercise along with ergonomic interventions like posture correction, modified posture for weight lifting and avoidance of prolonged standing. Treatment designed to train the activation of specific muscles thought to stabilize the lumbar – pelvic region. Functional disability outcomes were measured with The Roland Morris Disability Questionnaire and average pain intensity using McGill Pain Questionnaire. Statistics: PAIRED-T-TESTS: For comparing pre-and post- intervention changes within the group. Result and Conclusion: Based on statistical analysis and results the study concluded that Pilates based therapeutic exercise along with ergonomic interventions shows significant improvement in pain and functional disability in chronic non-specific low back pain and functional disability in chronic non-specific low back pain among nursing professionals.
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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 | 0.000 | 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) | 0.002 | 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".