Effectiveness of digital support intervention for self-management of low back pain among obese women
Bibliographic record
Abstract
Obesity is a growing public health concern. Obesity is one of several lifestyle factors that has been suspected of causing low back pain. Low back pain is an important clinical and public health problem. Digital interventions providing self-management information have been proposed as a promising mode of delivery for self-management interventions. A quantitative approach with pre-experimental research design was adopted for the present study conducted among 60 patients with low back pain among obese women by using purposive sampling technique. Demographic variables were collected pre-test was done by self-structured questionnaire. The investigator assessed the pre level of pain by using Quebec back pain disability scale. Then the group was trained to use mobile health application for 7 days. The mobile application consists of exercise, yoga poses, and diet-pattern. After one week the investigator assessed the post-test level of pain by using same Quebec back pain disability scale. The data were analyzed by using descriptive and inferential statistics. After intervention the experimental group value of posttest ‘t’ test value of t=21.547 was found to be statistically highly significant at p
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".