Effectiveness of Theory-based Educational Intervention on Low Back Pain Preventive Behaviors in Nursing Aid Staff
Bibliographic record
Abstract
Aim: Work -related Musculoskeletal Disorders (WMSds) are mainly associated with nurses' high physical demands.Training healthy behavior can reduce these disorders.This study aimed to evaluate the effect of educational intervention based on Social Cognitive Theory (SCT) on changing unhealthy behaviors leading to(LBP) in nursing aid staff working in Qom hospitals.Method and Materials: A quasi-experimental study was conducted from 2017 to 2018 with educational intervention based on SCT was performed on 452 nursing aid.Data collection tools were the questionnaire of SCT constructs, the LBP Prevention Behavior Questionnaire (LBPBPQ), the Quebec Back Pain Disability Scale (QBPDS), and the Visual Analog Scale (VAS) for LBP.The training was based on the four structures of self-efficacy, self-regulation, outcome expectation, and moral disengagement in groups of 20 to 30 individuals.Then the pre-and post-intervention data were compared through the statistical tests.Findings: After the intervention, SCT structures were increased significantly.The mean score of lumbar health behavior after training showed a significant increase from 32.59 to 32.57.The mean score of LBP after training decreased significantly from 5.17 to 3.98 and the mean score of physical disability of LBP decreased significantly after training.Conclusion: Educational intervention based on SCT reduces the severity of LBP and the consequent disability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".