Effect of Yoga Therapy on Low Back Pain Management Among Older Adults: Implications for Gerontology Counselling
Bibliographic record
Abstract
OBJECTIVE: This research aimed to determine the effect of yoga therapy in managing low back pain (LBP) among older adults. METHOD: 40 participants who were having low back pain were assessed. All participants completed baseline evaluation before beginning the Yoga intervention and at 6, 12 and 18 weeks. Participants completed a questionnaire titled Oswestry Low Back Pain Disability Questionnaire (ODQ). The statistical tool used for data analysis was within-and-between subjects ANOVA. RESULTS: The finding showed no significant difference in the baseline assessment for LBP between the treatment group and the waitlisted control group, F(1,38) = 2.697, P=.000, η2 = .066. The posttest assessment at 6th week revealed a significant reduction of LBP among older adults in the yoga treatment group compared with those in the waitlisted control group, F(1,37) = 3209.376, P = .000, η2 = .989. The assessment at 12th week revealed significant reduction in LBP among older adult in the yoga treatment group compared with those in the waitlisted control group, F(1,36) = 2389.154, P = .000, η2 = .985. The assessment at 18th week further revealed a significant reduction in LBP among older adult in the yoga treatment group compared with those in the waitlisted control group, F(1,36) = 2775.162, P = .000, η2 = .987. CONCLUSION: Yoga therapy is an effective intervention for managing low back pain among older adults. Thus, gerontology counsellors can provide help to older people with low back pain within the framework of Yoga therapy. Further studies are required to find out and corroborate the efficacy of Yoga Therapy in managing low back pain among older adults.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| 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".