Effectiveness of Core Muscle Strengthening in Bus Drivers having Low Back Pain using McGill Pain Questionnaire: Experimental Study
Bibliographic record
Abstract
Background: This study was developed to determine the effectiveness of core muscle strengthening exercises inreducing low back pain among bus drivers.Purpose: The purpose of the study is to determine the effectiveness of core muscle strengthening exercises inreducing low back pain among bus drivers.Materials and Methods: The research project was conducted as a Quasi experimental study in Saveetha collegeof physiotherapy, Thandalam Chennai and RK Multispecialty physiotherapy clinic, Ayanavaram, Chennai. Theinterventional group received core muscle strengthening exercise for 6 weeks. Following the six-week treatmentperiod, a post-test analysis was performed using the McGill Pain Questionnaire.Study period: from November2022 to July 2023.Results: The values were statistically analysed using paired ‘t-test and the Unpaired ‘t-test. The statistical analysismade with the quantitative data revealed a statistically significant difference before and after the intervention. Thepre-test mean value of the Mc gill questionnaire scale was 55.53 and the post-test value was 19.60. This shows thatthe result value obtained for disability is less after the intervention.Conclusion: From this result, it has been concluded that there is significant reduction in low back pain among busdrivers after the intervention.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".