Effect of Patient Transfer Training on Low Back Pain in Pre-hospital Emergency Medical Services Personnel
Bibliographic record
Abstract
Background and objectives: Pre-hospital emergency medical services (EMS) personnel are responsible for transferring patients.In case of improper patient handling, these individuals become vulnerable to various musculoskeletal problems including back pain.In this study, we aimed to evaluate the impact of an eight-hour training intervention about patient handling and transfer ergonomics on low back pain in pre-hospital EMS personnel working in the Golestan Province, Iran.Methods: This was a quasi-experimental study with a pre-test/post-test design.The study population consisted of 200 pre-hospital EMS personnel working in the Golestan Province, Iran.Overall, 40 EMS personnel were eligible to participate in the study.Data were collected using a demographic questionnaire, the Oswestry low back pain disability questionnaire and the Quebec back pain disability scale.The eight-hour training session was held by a research nurse, a physiotherapist and a physician.The subjects recompleted the Oswestry low back pain disability questionnaire and the Quebec back pain disability scale at baseline, four weeks and 12 weeks postintervention.The collected data were analyzed using SPSS 16 and descriptive statistics.Results: The mean age, body mass index and work experience was 38.6 ± 7.6 years, 25.9 ± 3.5 kg/m 2 and 8.27± 5.2 years, respectively.The mean score of functional disability reduced significantly from 35.9 ± 9 at baseline to 27.5 ± 2.5 and 19.6 ± 7 four weeks and 12 weeks after the intervention, respectively (P=0.0001).Furthermore, the mean pain score decreased from 38.7 ± 13.86 to 31.05 ± 10.75 one month post-intervention and to 22.4 ± 9.47 three months postintervention (P=0.0001).Conclusion: Our findings suggest that training intervention on ergonomic patient transfer and patient handling can reduce the rate of lower back pain in pre-hospital EMS personnel.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".